Localization UWB in BAN

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  • ANNE 2014

    MMOIRE DE MASTER / UNIVERSIT DE RENNES 1

    sous le sceau de lUniversit Europenne de Bretagne

    pour le grade de

    MASTER DE LUNIVERSIT DE RENNES 1

    Mention : Systmes Embarqus

    cole doctorale MATISSE

    prsente par

    Gia-Minh HOANG

    Prpare lIRISA (UMR 6074) Institut de recherche en informatique et systme alatoire

    cole Nationale Suprieure des Sciences Appliques et de Technologie

    Localization via Ultra

    Wide Band Radio for

    Wireless Body Area

    Sensor Networks

    Mmoire soutenue Lannion le 18 Septembre 2014

    devant le jury compos de :

    Olivier SENTIEYS Professeur lUniversit de Rennes 1 / examinateur

    Arnaud TISSERAND Chercheur au CNRS / examinateur

    Olivier BERDER Professeur lUniversit de Rennes 1 / examinateur

    Matthieu GAUTIER Matre de Confrences lUniversit de Rennes 1 / encadrant

    Antoine COURTAY Matre de Confrences lUniversit de Rennes 1 / co-encadrant

  • Abstract

    Wearable devices have strongly emerged as a key technology in a wide variety ofunforeseen applications. One of the most growing research interests is WirelessBody Area Sensor Network (WBASN). In this context, precise determination ofwireless sensors positions responses to the great needs in numerous typical ap-plication fields such as medicine, security, sport science and entertainment. . . Inour work, we are inspired by the centimeter-level positioning capability of Ul-tra Wide Band (UWB) radio. This attractive technology has been standard-ized for localization in GPS-denied environments like WBASN. This Masterthesis presents cutting-edge methods for performing the accurate localizationin WBASN. By fully exploiting its unique features, a novel cooperative-cum-constrained localization algorithm is developed considering the wireless sensordeployments. Simulation results show absolute agreement with theoretical pre-diction and improvement over previous studies by Hamie et al and Mekonnen etal. Finally, we also partially validate our algorithmic proposals using real UWBplatforms.

    Keywords: Ultra Wide Band, Wireless Body Area Sensor Networks, IndoorLocalization, Cooperative Networks, IEEE 802.15.4, IEEE 802.15.6.

    i

  • Acknowledgements

    First, I would like to offer my sincerest gratitude to Prof. Olivier SENTIEYSfor welcoming me to CAIRN project-team of laboratory IRISA. Without yourhelp, I would not have an opportunity to study in France.

    Foremost, I would like to thank Prof. Matthieu GAUTIER and Prof. An-toine COURTAY, my thesis supervisors, for accepting me as their protege andguiding me to complete this Master internship. They have always given me use-ful guidance, listened patiently to my ideas with my limited English and Frenchproficiency, read and reviewed carefully my work as well as my report. It is mygreatest honor to have them as my advisors.

    My special thanks give to Prof. Benot DENIS (CEA-LETI) for the fruitfuldiscussions and invaluable documents. Many thanks to Prof. Jean-ChristopheCOUSIN (Telecom ParisTech) for the constructive comments on my work.

    Thanks to Benjamin and Albert for accompanying me on the trips to Rennesevery weeks in the first semester.

    I would like to thank my grand family. They always stand by my side throughthe good times and bad, encourage me with their best wishes.

    Last but not least, I would also like to give my warmest thanks to my secondfamily in Lannion. They have helped me to embark a new life in France, sharedtheir life experiences, offered me a nice atmosphere in Lannion like a big family.

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  • Contents

    1 Introduction 1

    2 UWB-based Localization in WBASN 32.1 UWB Technology: an Idea to Localization in WBASN . . . . . . 32.2 UWB Localization Approaches for WSN . . . . . . . . . . . . . . 4

    2.2.1 Measurement Step . . . . . . . . . . . . . . . . . . . . . . 42.2.2 Position Estimation Step . . . . . . . . . . . . . . . . . . 7

    2.3 State-of-the-art on Localization Algorithms in WBASN . . . . . 92.3.1 Constrained Localization . . . . . . . . . . . . . . . . . . 92.3.2 Cooperative Localization . . . . . . . . . . . . . . . . . . 102.3.3 Combined Cooperative-Constrained Localization . . . . . 11

    2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

    3 Cooperative-cum-Constrained Algorithm 133.1 Types of WBASN Localization . . . . . . . . . . . . . . . . . . . 133.2 Relative Localization in WBASN . . . . . . . . . . . . . . . . . . 14

    3.2.1 Relative Localization System Model . . . . . . . . . . . . 143.2.2 Relative Localization Algorithms . . . . . . . . . . . . . . 153.2.3 Simulations and Results . . . . . . . . . . . . . . . . . . . 21

    3.3 Absolute Localization in WBASN . . . . . . . . . . . . . . . . . . 293.3.1 Absolute Localization System Model . . . . . . . . . . . . 293.3.2 Absolute Localization Algorithms . . . . . . . . . . . . . . 303.3.3 Simulations and Results . . . . . . . . . . . . . . . . . . . 31

    3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

    4 Experiments 374.1 Evaluation Platform . . . . . . . . . . . . . . . . . . . . . . . . . 374.2 Experimental Configurations . . . . . . . . . . . . . . . . . . . . 37

    4.2.1 Use-case Configuration . . . . . . . . . . . . . . . . . . . 374.2.2 Platform Calibration . . . . . . . . . . . . . . . . . . . . . 38

    4.3 Experimental Results and Discussion . . . . . . . . . . . . . . . . 414.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

    5 Conclusions and Perspectives 455.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.2 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

    Appendices 47

    v

  • CONTENTS

    A Evaluation of Numerical Methods 49

    vi

  • List of Figures

    2.1 Spectrum allocation for various wireless systems . . . . . . . . . 42.2 Two-step positioning/localization. . . . . . . . . . . . . . . . . . 42.3 Two-way ranging protocol. . . . . . . . . . . . . . . . . . . . . . 52.4 Minimum standard deviation versus SNR and effective bandwidth. 62.5 Trilateration technique for 2D localization. . . . . . . . . . . . . . 82.6 Trilateration in case of noisy measurements. . . . . . . . . . . . . 8

    3.1 Relative localization system for WBASN. . . . . . . . . . . . . . 153.2 Constraints in WBASN. . . . . . . . . . . . . . . . . . . . . . . . 173.3 Cooperative scenarios . . . . . . . . . . . . . . . . . . . . . . . . 193.4 Sensor deployment in the biomechanical model for relative local-

    ization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.5 Relative localization RMSE and GDOP in case of range = 5 cm. 253.6 Empirical CDF plot of the localization error of the node T1s

    position estimate in case of range = 5 cm. . . . . . . . . . . . . . 263.7 Empirical CDF plot of the localization error of the node T2s

    position estimate in case of range = 5 cm. . . . . . . . . . . . . . 273.8 Trade-off between the accuracy and the number of cooperative

    measurements/constraints in case of range = 5 cm. . . . . . . . . 283.9 Trade-off between the accuracy and the processing time in case

    of range = 5 cm. . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.10 Absolute localization system for WBASN. . . . . . . . . . . . . . 303.11 Sensor deployment in the biomechanical model for absolute lo-

    calization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.12 Absolute localization RMSE and GDOP of the node T1s estimate

    in case of range = 5 cm. . . . . . . . . . . . . . . . . . . . . . . . 323.13 Empirical CDF plot of the localization error of the node T1s

    position estimate in case of range = 5 cm. . . . . . . . . . . . . . 333.14 Trade-off between the accuracy and the number of cooperative

    measurements in case of range = 5 cm. . . . . . . . . . . . . . . 343.15 Trade-off between the accuracy and the processing time in case

    of range = 5 cm. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    4.1 DecaWaves IR-UWB EVK1000 kit with its package. . . . . . . . 384.2 Two-way ranging calculation. . . . . . . . . . . . . . . . . . . . . 404.3 Calibrated antenna delay for different distances. . . . . . . . . . 424.4 Bias of ranging measurements with calibrated platforms. . . . . . 424.5 Deviation of ranging measurements with calibrated platforms. . . 43

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  • LIST OF FIGURES

    A.1 Simple localization scheme. . . . . . . . . . . . . . . . . . . . . . 50A.2 Mean of distance errors as a function of the standard deviation

    of ranging errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50A.3 Localization RMSE as a function of the standard deviation of

    ranging errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

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  • List of Tables

    2.1 Current localization technologies. . . . . . . . . . . . . . . . . . . 7

    3.1 Evaluated relative localization algorithms. . . . . . . . . . . . . . 243.2 Evaluated absolute localization algorithms. . . . . . . . . . . . . 32

    4.1 Configuration modes. . . . . . . . . . . . . . . . . . . . . . . . . 38

    ix

  • Mathematical Notations

    A scalar

    A vector or matrix

    {Ai}Ni=1 set of N vectors/matrices Ai, i = {1, 2, . . . , N}AB length/magnitude/Euclidean norm of vector AB

    xi

  • Chapter 1

    Introduction

    Wireless Body Area Sensor Network (WBASN) is gaining widespread attentionin numerous application disciplines. This network is a short-range wireless net-work composed of small, low-power, wearable or implanted electronic sensorson, around or inside the human body. It supports the data communication overshort distances with the other sensors or with the data center for many specialpurposes. Thus, various potential mobile, personal and body-centric applica-tions are promised to fulfill the market needs in short term. Some of typicalapplication fields include medicine and health care (e.g. rehabilitation, assis-tance to medical diagnosis and endoscopy, patients posture monitoring, biome-chanics), search and rescue (e.g. remote posture detection of injured athletes,emergency responders, miners, victims of natural disaster, and fire-fighters. . . ),security and military (e.g. indoor positioning and navigation of authorized peo-ple, military personnel in high-security areas), and entertainment (e.g. sportperformance science, 3D character animation for film, game, TV. . . ). In theseapplications, the localization of the sensors is required and the accuracy playsa key role.

    In the context of WBASN, the requirement of accuracy is highly demandingthat many conventional localization systems such as well-known Global Posi-tioning System (GPS) fail to satisfy. Thus, since the last decade of the lastcentury, a great deal of time and efforts have gone into investigating new local-ization systems using terrestrial networks (e.g. LORAN-C, GSM, UMTS. . . ),local networks (e.g. Wi-Fi, ZigBee, Bluetooth. . . ) [1], optical systems (e.g. Co-damotion tracking system), inertial systems (e.g. Xsens MVN), and ultrasoundsystems. . . However, increasing the accuracy as regards the other constraints im-posed by WBASN like low-power consumption, low implementation cost, lowcomputation and wearable sensors is still a major issue to tackle.

    Due to critical requirements of accurate, low-cost, and low-power localiza-tion for the deployment of WBASN, a dedicated technology and a localizationalgorithm must be defined. In [2], by exploiting the very large signal bandwidthof Ultra Wide Band (UWB) radio, the UWB nodes that employ the time-basedlocalization techniques can estimate their relative distances and then computethe target nodes position with a very high time resolution. This outstandingcharacteristic also enables robust communication in dense multipath environ-ments [3]. Furthermore, low-power and low-cost implementation of Impulse Ra-dio Ultra Wide Band (IR-UWB) communication systems meet the key require-

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  • CHAPTER 1. INTRODUCTION

    ments for wearable sensors. These aspects make UWB an attractive technologyto improve localization accuracy in a GPS-denied environment like WBASN.Furthermore, it is found that under the circumstances, the geometrical con-straints imposed by the human body lead to performance gain [4][5]. One theother hand, the accuracy of localization can be remarkably improved by coop-eration between wireless nodes [3].

    The main goal of our Master thesis is to perform the localization in WBASNenvironment by investigating UWB radio and introducing the novel Cooperative-cum-Constrained (CC) algorithm for body-strapped nodes. This proposal is the-oretically evaluated in terms of accuracy, complexity and empirically validatedusing real IR-UWB platforms.

    The remainder of this Master thesis is structured as follows:Chapter 2 describes the UWB technology briefly and its unique advantages

    to WBASN. The typical radio localization process for general Wireless SensorNetwork (WSN) and also for specific WBASN is provided specifically. The state-of-the-art constrained and cooperative localization algorithms are introducedafterwards.

    Chapter 3 concentrates on our proposed cooperative-cum-constrained local-ization algorithm for WBASN.

    Chapter 4 accounts for extensive experiments that completely characterizethe IEEE standard 802.15.4-2011 UWB technology, its reliability and capabilitybased on DecaWaves IR-UWB platforms [6] and validate our proposals. Thispart also discusses the fundamental weaknesses of DecaWaves.

    Finally, chapter 5 draws general conclusions and discloses some researchperspectives for future work.

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  • Chapter 2

    UWB-based Localization inWBASN

    In this chapter, we provide a concise overview of the UWB technology andits benefits for WBASN, then describe in detail the UWB-based localizationapproaches. After that, the state-of-the-art localization algorithms designed forWBASN are introduced before ending with the conclusion.

    2.1 UWB Technology: an Idea to Localizationin WBASN

    UWB characterizes communication systems with instantaneous spectral occu-pancy in excess of 500 MHz or a relative bandwidth of more than 20% [2][7]. Asbandwidth is a scare resource, in 2002, the Federal Communications Commission(FCC) in the United States released a huge new bandwidth (3.110.6 GHz)where power spectral density must not exceed -41.3 dBm/MHz in order not toadversely influence the operation of other systems as shown in Figure 2.1 [8].Such new bandwidth permits UWB systems to emit low power and ultra-shortduration (sub-nano second) pulses and provides both communications and radarapplications with unique advantages. Some of them include [3][7][8]:

    Obstacle penetration capabilities.

    High precision ranging at centimeter level.

    Low electromagnetic pollution and reduced interference to other systems.

    Small size and low energy/power consumption.

    It is worth reminding that a WBASN is an autonomous network at the humanbody scale which constitutes a group of wearable, low-power, hardware con-strained wireless devices strapped to or implanted into an active body. There-fore, the wealth of UWB positive features that we have investigated fulfills therequirements of localization in WBASN exactly.

    3

  • CHAPTER 2. UWB-BASED LOCALIZATION IN WBASN

    GP

    S

    PC

    S

    ISM

    Ban

    d

    U-N

    II B

    an

    d

    UWB Spectrum

    1.6 1.9 2.4 3.1 5 10.6

    41

    Frequency (GHz)

    Em

    itte

    d S

    ign

    al

    Po

    wer

    (dB

    /MH

    z)

    FCC Part 15 Limit

    802.11a WLAN

    Cordless Phone

    Bluetooth,

    802.11b WLAN,

    Cordless Phone,

    Microwave Ovens

    Note: not to scale

    Figure 2.1: Spectrum allocation for various wireless systems (Source: CertifiedWireless USB Developers Conference 2006).

    2.2 UWB Localization Approaches for WSN

    In general WSN and specific WBASN, the unknown location of a node (calledthe target node, the tag, or the agent) is determined in two steps as shown inFigure 2.2. The first one is measurement step. One or more position-related met-rics are measured in this step. Then, the relative distances between each pairof nodes can be inferred from these metrics. The second step called positionestimation step aggregates these distance measurements as input of a positionestimator to produce the target nodes position in a particular predefined co-ordinate system as output. The following sections are dedicated to discuss eachstep in more detail.

    Estimation of

    position-related

    parameters

    Position

    estimation

    Received

    signals Position estimate RSSI

    AoA

    ToA

    TDoA

    RToA

    Figure 2.2: Two-step positioning/localization [8].

    2.2.1 Measurement Step

    In the first step, a target node exchanges packets with a few neighbouring nodeswhose positions are known a priori (say, the reference nodes, the beacons, orthe anchors). From the physical waveforms corresponding to these packets, thenode can extract information regarding its location relative to that of the otherby measuring or estimating one or more signal parameters [3][8]. Depending

    4

  • 2.2. UWB Localization Approaches for WSN

    on the localization technique, they can be Received Signal Strength Indication(RSSI), Angle of Arrival (AoA), Time of Arrival (ToA), Time Difference ofArrival (TDoA), or Round-Trip Time of Arrival (RToA). The AoA techniqueaddresses the direction of incoming signals to estimate the angles between atarget nodes and a number of anchors. Alternatively, the RSSI and the time-based techniques (i.e. ToA, TDoA, and RToA) measure the ranges betweeneach pair of nodes by recording the power and the Time of Flight (ToF) or itsvariants of the received signal, respectively [2].

    As discussed in [2][7][8], the AoA and the RSSI are not relevant techniquesfor UWB systems. AoA-based techniches are costly and more vulnerable in mul-tipath environments when employing UWB signal while RSSI-based techniquesare sensitive to environment, irrelevant the advantages of large bandwidth andinaccurate. Furthermore, time-based approaches can provide a reliable meansfor UWB localization. Nevertheless, the time-based schemes require a very pre-cise common time reference between nodes that is extremely difficult to achievein reality. More particularly, the ToA technique demands the all-synchronizednodes and the TDoA scheme does exact the synchronization among referencenodes. The most interesting approach RToA, however, employs a two-way rang-ing protocol [9] to avoid the synchronous issue and encourages us to adopt itto study throughout this thesis. Straightforwardly, the distance between twonodes (call, node A and B) can be measured accurately in 4 phases, as shownin Figure 2.3.

    00 MOIS 2011EMETTEUR - NOM DE LA PRESENTATION - 4

    Node A

    Node BTime

    ,

    , ,

    ,

    Total transaction

    Unsynchronized

    clocks

    Figure 2.3: Two-way ranging protocol [9].

    1. Node A starts its clock and sends a packet to node B at time tA,transmit.

    2. Node B receives this packet at time tB,receive and activates its own clock.

    3. Node B takes some time to process the packet and responds with anotherone at time tB,transmit = tB,receive + tprocess, and tprocess is a process-ing time at node B that is preset or communicated in the response packetto node A. Simultaneously, it stops its clock.

    4. Node A latterly receives the packet from node B at time tA,receive andcomputes the distance dAB by the following relation:

    dAB =(tA,receive tA,transmit tprocess) c

    2, (2.1)

    where c = 299, 792, 458 m/s is the well-known speed of light.

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  • CHAPTER 2. UWB-BASED LOCALIZATION IN WBASN

    Therefore, the performance of the distance measurement strongly relies onthe ToA detection/estimation and the processing time. One simple but efficientmechanism to estimate the processing time was proposed in [10]. And typicalToA estimators involve in matched filtering, correlation operations, deconvolu-tion approaches, window and threshold methods [5][8]. Accordingly, the theo-retical limits for ToA estimation are considered for ease of study.

    For the simplest single-path Additive White Gaussian Noise (AWGN) chan-nel, the Cramer-Rao Lower Bound (CRLB) for a non-Bayesian distance estimate

    d from ToA estimation provides the following inequality [11]:V ar(d) c

    2

    2piSNR

    , (2.2)

    where c is the speed of light again, SNR denotes the Signal-to-Noise Ratio(SNR), and represents the effective bandwidth defined in the following expres-sion:

    =

    +

    f2|S(f)|2df/ +|S(f)|2df

    12

    , (2.3)

    where f denotes the frequency and S(f) is the Fourier transform of the trans-mitted signal.

    It can be shown in the inequality (2.2) that the accuracy of a time-basedapproach can be augmented by increasing the SNR and/or the effective band-width. For example, considering the minimum standard deviation of distanceestimation according to the CLRB in (2.2) for various levels of SNR and effectivebandwidths pictured in Figure 2.4, it can be seen clearly that the theoreticalbounds are in the order of a few centimeters for commonly achievable SNR val-ues when performing ToA measurements via UWB radio. More particularly, anaccuracy of 3 cm can be achieved in case of deploying 1 GHz bandwidth UWBradio and SNR of 0 dB.

    200

    20

    0.5

    1

    1.5

    102030405060

    SNR (dB) (GHz)

    CRLB

    (cm)

    Figure 2.4: Minimum standard deviation versus SNR and effective bandwidth.

    6

  • 2.2. UWB Localization Approaches for WSN

    In comparison with other existing localization technologies given in Table 2.1in terms of accuracy, the UWB-based systems stand as a potential candidatefor localization. This high resolution in time thanks to this extreme band-width offers fairly accurate ranging measurement and position estimation after-wards when employing time-based techniques. Considering other requirementsof WBASN (e.g. small size, low-power consumption, hardware constraints, lowcost. . . ), we can theoretically validate that UWB technology is very favorablein our context (e.g. localization in WBASN).

    Table 2.1: Current localization technologies [8].

    Technology Accuracy Remarks

    Satellitesystems

    GPS 15m (9599%)Expensive, does not workindoors

    A-GPS 10mModified handsets that usea GPS receiver

    Terrestrialnetworks

    LORAN Better than 460mLORAN-A (World War II)LORAN-C (1950s)

    eLORAN 820mInstalled in US (2004)and north-west Europe.

    Localnetworks

    WLAN 34.3m (50%) Microsoft RADARZigbee Room-level Spot (Inner Wireless, Inc.)

    Ultrasound 9 cm (95%) Active Bats

    Opticalsystems

    Cameravision

    Around 0.05mm Codamotion

    Laser Around 1mm Indoor GPS (Metris, Inc.)

    2.2.2 Position Estimation Step

    In the second step, ranging measurements are gathered and processed througha localization algorithm to produce the targets position.

    Range Accuracy and Geometric Considerations

    As discussed above, ranging measurements between a target node and a numberof reference nodes are combined to produce a targets position. This techniqueis called trilateration, which is dedicated to ToA and RToA techniques. ForTDoA approach, a variant of trilateration called multilateration is utilized [8].Since we adopted the RToA technique, trilateration method is addressed in ourstudy. The concept is to determine the intersection of three circles specifiedby the ranging measurements with three reference nodes in 2D localization asillustrated in Figure 2.5. In 3D case, one more reference node is required toreach a unique solution.

    Nonetheless, a critical issue that must be taken into account is that the mea-surement noise is inevitable. Other sources include multipath channel, blockage,interference, clock drifts, and environmental effects [3]. Accordingly, the estima-tor can not determine the intersection of the three circles and definitely producethe targets position with error. This resulting position error is affected by the

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  • CHAPTER 2. UWB-BASED LOCALIZATION IN WBASN

    T

    R2

    R3

    R1

    Figure 2.5: Trilateration technique for 2D localization. The ranges between tar-get node T and reference nodes R1, R2, and R3 are aggregated to compute theposition of the target one [8].

    placement of the reference nodes (i.e. the geometry of reference nodes relativeto the target node) as illustrated in Figure 2.6 [12]. In general, the referencenodes are placed on a geometry whose corners are equally distributed on aunit spherical surface (i.e the tetrahedron for 4 reference nodes) [8].

    R1

    R2

    R3

    R1

    R3

    R2

    Figure 2.6: Trilateration in case of noisy measurements. Two examples with sameranging noise leading to radically different position errors due to the placementof the reference nodes [12].

    As the measurements become less reliable in practice, the complexity of thelocalization estimator should be increased [13].

    Types of Estimators

    There are various approaches for estimating a parameter from an observa-tion/measurement y = f() + n, where f() is a deterministic function andn represents a measurement noise whose distribution is known or assumed a

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  • 2.3. State-of-the-art on Localization Algorithms in WBASN

    particular form (e.g. Gaussian distribution). Typically, the estimators can beclassified into non-Bayesian and Bayesian approaches.

    Non-Bayesian estimator treats as a fixed but unknown parameter. Twocommonplace non-Bayesian estimators are the Least Square Error (LSE) esti-mator and the Maximum Likelihood (ML) estimator. They are as follows [3][8]:

    LS = arg miny f()2, (2.4)

    ML = arg max

    pY |(y|). (2.5)

    where pY |(y|) denotes the conditional probability density function of obser-vation y given parameter .

    Bayesian estimator assumes to be random variable with a (assumed)known a priori distribution p(). This Bayesian approach seeks to estimatethe posterior density p|Y (|y). Two common Bayesian estimators are the Min-imum Mean Squared Error (MMSE) estimator and the Maximum A Posteriori(MAP) estimator that are as follows [3][8]:

    MMSE =

    p|Y (|y)d, (2.6)

    MAP = arg max

    p|Y (|y). (2.7)The exhaustive details of these estimators are described in [14]. However,

    in practice, the posterior density p|Y (|y) is difficult to determined. Someapproximate techniques to estimate this density have been investigated in [3].Yet they are fairly complex and the trade-off is that the flexibility with regardto computation and their energy consumption prevents from employing these inthe context of localization in WBASN.

    Accordingly, within the scope of the Master thesis, the ML estimator isadopted due to its simplicity as well as extensive applications in position esti-mation [3][4][5][8]. Moreover, [15][16] yield that the ML estimator asymptoticallyachieves the Cramer-Rao lower bound.

    2.3 State-of-the-art on Localization Algorithmsin WBASN

    This section aims to introduce the improvements to the localization in WBASN.In the following, our interests include constrained localization and cooperativelocalization. Our hybrid localization algorithm is also proposed at the end ofthis section.

    2.3.1 Constrained Localization

    This first improvement is proposed to adapt the ML localization specifically intothe WBASN localization to compensate for the degraded positioning accuracyin this hostile environment.

    In the studies of [4] and [5], it is found that under circumstances, the ge-ometrical constraints imposed by the human body lead to performance gain.

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  • CHAPTER 2. UWB-BASED LOCALIZATION IN WBASN

    They introduce the fixed Euclidean distances between some special target sen-sors to the likelihood optimization problem mentioned in the previous sectionas constraints. More precisely, there exist some positions in WBASN where sen-sors placed at these maintain fixed relative distances to another. Otherwise, therelative range between each pair of them are independent of the body gesture,the moving direction as well as the velocity (i.e. the constant distances betweeneach elbow and corresponding wrist, between each knee and corresponding in-step. . . ). In addition, the wearable devices have restricted degrees of freedomwith respect to the torso (i.e. the arm-strapped sensors cannot get the positionsthat are 1 meter away from the torso). These constraints can provide additionalinformation, which increases the accuracy of the localization.

    Even though the constrained ML localization for WBASN has been inves-tigated in [4][5], there remain some downsides in these studies that demandfurther improvements. Specifically, [4] did not take full advantage of possibleconstraints imposed by human body. Only the fixed distances relating to thesensors on the elbow joints and on the corresponding wrists were taken intoaccount. On the other hand, [5] proposed the full-exploited constrained MLpositioning but this work was restricted to 2D localization. One claimed disad-vantage of both studies is that in reality, it is impractical to place the sensors onthe body joints/bends such as elbows and knees to obtain the constraints due tothe difficulty to install and keep the sensors stand still relatively on the parts ofthe moving body. One typical example of the sensor placement on human bodyis mentioned in the Xsens MVN system [17]. As a result, we may not take fullbenefit from already existing constraints imposed by WBASN like [5].

    Therefore, in our work, we propose our modifications to these existing con-strained ML localization including the full exploitation of body constraints asregards the placement of the sensors. By studying analytic geometry, novel con-straints are proposed. The details are described mathematically in Chapter 3.

    2.3.2 Cooperative Localization

    The second improvement is suggested for taking full advantage of spatial diver-sity and measurement redundancy by cooperative techniques [18]. Cooperativeapproaches have received extensive interest and been put into practice in gen-eral WSN. Many researches have been carried out and arrived at the concurrentconclusion that in harsh environments, the accuracy of position information canbe improved radically by cooperative communications [3][8][19][20]. But noneof them have addressed to the specific WBASN. Additionally, according toour knowledge, there is a little research in cooperative localization in WBASN.Hence, in our work, we aim to inherit and adapt these studies to our context(i.e. localization in WBASN). The basic idea of cooperative approaches relieson direct peer-to-peer communications between target nodes that are ignored innon-cooperative ones. Besides, WBASN encounters with several serious prob-lems of disconnection and Non-Light-Of-Sight (NLOS) reception that cause er-roneous ranging measurements. These problems can be compensated by allowingunlocalized nodes to make measurements with other unlocalized nodes, whichare Light-Of-Sight (LOS). These information redundancies are an advantage forfiner gain accuracy and robustness of the localization system.

    Moreover, cooperative approaches can take advantage of mesh topology. Ac-cordingly, various cooperative schemes are studied in terms of improving the po-

    10

  • 2.4. Conclusion

    sitioning accuracy as regards computational complexity and over-the-air traffic(i.e. the number of measurements/exchanged packets).

    2.3.3 Combined Cooperative-Constrained Localization

    Localization has been studied intensively in general WSN but not much inspecific WBASN. For localization in the context of WBASN, several improve-ments have been proposed. Precisely, [5] only addressed the constrained local-ization and limited to the 2D localization whereas [4][21] developed it to 3Denvironment. In addition to improve the constrained localization in [4][5][21],as discussed above, cooperative approaches are attractive to gain localizationperformance [3]. Therefore, our proposal will merge the two constrained andcooperative techniques into one hybrid algorithm. We named it as Cooperative-cum-Constrained (CC) localization algorithm. In the aspect of body constraints,our proposal considers the practical disposition of the sensors without scarifyingthe possible constraints, which is missing in the previous efforts by [4][5]. On theother hand, the well-known cooperative techniques is applied selectively in thecontext of WBASN with regards the complexity, the cooperative links betweensensors to achieve the LOS configuration most of the time. . .

    2.4 Conclusion

    In this chapter, we have investigated the context of our work. The majority ofits uniquely attractive features satisfying the strict conditions of WBASN wereexposed.

    The localization process are then presented step-by-step to picture the wholesystem. The measurement step was examined thoroughly. The theoretical limitof time-based localization was also analyzed. Besides, we have dealt with geo-metric considerations and some possible position estimators.

    Finally, the state-of-the-art localization algorithm was addressed. Severallimitations of some existing works were exposed and the improvements weresuggested. Our proposed algorithm will be simulated in various scenarios andcompared with the conventional one and other existing studies.

    11

  • Chapter 3

    Cooperative-cum-ConstrainedAlgorithm

    In this chapter, firstly, the typical WBASN localization techniques are intro-duced. Secondly, our proposed cooperative-cum-constrained algorithm is thenexposed completely. Finally, the performance results are evaluated through sim-ulation as compared to the existing techniques concerning both accuracy andcomplexity.

    3.1 Types of WBASN Localization

    WBASN localization can be classify according to the placement of the referencenodes or the coordinate reference system [3][4][5][8][21]. Depending on the use-cases, relative or absolute localization can be deployed. They are as follows:

    Relative Localization refers to the system where not only target nodes butalso reference nodes are strapped or attached onto the body. These refer-ence devices are mounted to the fixed parts of the body to form a LocalCoordinate System (LCS). In this LCS, we can assume that the positionsof these reference sensors are known a priori. From that, the relative rangesbetween each target node and all reference nodes are determined by ToAestimation, thus, this information can be combined with the known refer-ence nodes positions to localize the corresponding target devices in theLCS [5]. Note that this LCS is variant according to the body movements.One obvious advantage of this scheme is that no infrastructure is required,hence, the deployment is simple.

    Absolute Localization refers to the localization in a single predeterminedcoordinate system [3]. Unlike the relative localization, in this case, thereference nodes are installed at fixed positions in the vicinity to the bodyto define a Global Coordinate System (GCS). With this infrastructure, weare able to acquire the absolute positions of the target sensors. One mainadvantage of absolute localization is to enable the navigation function,

    13

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    besides human motion capture. Other strength is the flexibility to placethe set of reference devices to seek the best solution for the localization.Despite these benefits, this type of localization requires the additionalinfrastructure, which may be costly.

    Regardless of different coordinate systems, most of the estimators can be appliedto these localization schemes. These couple types will be examined thoroughlyin the following.

    3.2 Relative Localization in WBASN

    3.2.1 Relative Localization System Model

    As have been mentioned in the previous section, relative localization in WBASNindicates a localization system where the set of reference nodes is attached orimplanted directly into the moving body at some special positions that areindependent of the body mobility or gestures (e.g. usually on the torso). Inother words, this set of reference nodes defines a LCS on the human body.Therefore, the positions of the body mounted nodes can be known a priori.Target nodes are localized relatively from this set by performing the rangingmeasurements with these reference nodes. Then the positions of these can beobtained through trilateration with the help of an estimator.

    Figure 3.1 illustrates a typical relative localization scenario. In this figure,the green spheres represent the references nodes. On the other hand, the targetnodes are denoted by the reds. We note that the reference nodes are placed onthe known positions that are not influenced by the body attitude or mobilitysuch as on the chest, on the back and on the waist. The number of reference de-vices should not be less than 3 for 2D positioning and 4 for 3D case without anyprior knowledge about the target nodes positions. Besides, as discussed in theprevious chapter, the best placement of reference nodes should be tetrahedral,hence, the 4 reference sensors are arranged as follows: one on the chest, one onthe back, the two remaining sensors on the hip. For the disposition of the targetdevices, we avoid attaching these on the joints/bends due to sensors possibledisplacement. Instead, the disposition of target nodes relies on the Xsens MVNsystem [17].

    Notation: Throughout this thesis, we will use the following notation. {Ti}Nti=1represents the unknown positions of the Nt target sensors, {Ri}Nri=1 indicatesthe known a priori positions of the Nr reference devices in the LCS on the torso,

    and {Ji}Nji=1 denotes Nj special body joints/bends whose positions with respectto the LCS are also known a priori (e.g. the shoulder and the hip joints) asillustrated in Figure 3.1. Intuitively, the positions of nodes as well as joints can be

    represented in vector form as Ri =[R

    (x)i , R

    (y)i , R

    (z)i

    ]T, Ti =

    [T

    (x)i , T

    (y)i , T

    (z)i

    ]T,

    and Ji =[J

    (x)i , J

    (y)i , J

    (z)i

    ]T. Let =

    [T

    (x)1 , T

    (y)1 , T

    (z)1 , . . . , T

    (x)Nt, T

    (y)Nt, T

    (z)Nt

    ]Tbe

    the estimate vector, which consists of unknown parameters. In the same way, wedenote the collection of peer-to-peer ranging measurements by the vector d =[d11, d12, d13, . . . , d1Nt , d21, . . . , dNrNt

    ]T, where dij is the ranging measurement

    between reference node i and target node j. So an important point to rememberis that dij 6= dji,i 6= j.

    14

  • 3.2. Relative Localization in WBASN

    1

    2

    3

    4

    1

    2 3

    4

    x

    y

    z

    Reference node

    Reference node on the back

    Target node

    Body joint/bend

    5

    6

    7

    8

    9

    10

    1

    2

    4

    5

    3 67

    8

    9

    10

    11

    12

    Figure 3.1: Relative localization system for WBASN.

    As soon as these ranging measurements are determined by the IR-UWB ToAinformation, our task is to estimate the positions of the target sensors in theLCS. Briefly, given the positions {Ri}Nri=1, some special joints positions (i.e.J1, J4, J7, and J10 as in Figure 3.1) in the LCS and the ranging informationd, the objective is to estimate the vector .

    3.2.2 Relative Localization Algorithms

    Before presenting the constrained as well as the cooperative localization algo-rithms, it is worth beginning with the standard ML approach which is the coreof the localization estimator.

    Standard ML Estimator

    As introduced in equation (2.5), the ML estimator pays attention to the statisticsof the noise sources and maximizes the following likelihood function [5][8]:

    p(d|)

    =

    Nri=1

    Ntj=1

    p(dij |

    ), (3.1)

    15

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    where p(|) denotes the conditional probability density function given param-eter . And the ML estimator is as follows:

    = arg max

    Nri=1

    Ntj=1

    p(dij |

    ). (3.2)

    The measured range dij between reference node i and target node j can bemodeled with assumption that LOS links result in an unbiased ranging mea-surement with Gaussian error and NLOS links are reconstructed as a positivelyuniformly distributed biased ranging measurement with Gaussian error [4] or asa exponentially distributed measurement noise [8], i.e.

    dij =

    {dij + nij , for LOS linkdij + nij + bij/dij + bij , for NLOS link

    , (3.3)

    where dij and dij are the measured and the true range between reference node iand target node j, nij is a zero mean Gaussian random variable with a standarddeviation ij , and bij is a ranging measurement offset introduced by NLOSconfiguration when employing ToA estimation.

    Let consider a simpler scenario where the ranging errors are modeled ascentered independent Gaussian variables in LOS links and as constant biaseswith Gaussian noises in NLOS links, i.e.

    dij N(RiTj+ ijbij , 2ij) , (3.4)

    whereRiTj = dij denotes the real distance between the reference node i and

    the target node j, ij is a term to identify the LOS or NLOS condition as follows:

    ij =

    {0 , for LOS link1 , for NLOS link

    , (3.5)

    then the likelihood function of takes the form:

    p(d|)

    =

    Nri=1

    Ntj=1

    12pi2ij

    exp

    (dij (Ri Tj)T (Ri Tj) ijbij

    )222ij

    (3.6)

    As a result, the ML has now the formula of the non-linear Weighted LeastSquares (WLS) estimator which yields:

    = arg min

    Nri=1

    Ntj=1

    wij

    (dij (Ri Tj)T (Ri Tj) ijbij

    )2, (3.7)

    where wij= 1/2ij plays the role of weight which reflects the accuracy and the

    reliability of the measurement dij . Accordingly, when the measurements havedifferent uncertainties, unreliable ones are down-weighted in the merit functionor objective function and vice versa, trusted ones are over-weighted. This WLSestimator is very popular in a wide range of localization problems, including inthe context of WBASN.

    16

  • 3.2. Relative Localization in WBASN

    The vector of unknown parameter in the expression (3.7) cannot be de-scribed in close-form solution. Numerical methods are employed to solve thisnon-linear problem instead. Common iterative techniques involve well-knowngradient descent methods, relaxed optimization methods, conjugate gradientmethods, and Quasi-Newton methods [22][23]. In the scope of this work, threecommon algorithms including Nelder-Mead method, also known as the down-hill simplex, Powells method, and Fletcher-Reeves method are investigated andcompared by simulation-based results. The first algorithm has been alreadyemployed in the MATLAB function fminsearch() while the detailed implemen-tations of the 2 latter ones are presented in [23]. The evaluation framework ofthese algorithms in the context of localization is presented in the Appendix A.The performance of these algorithms appears not very difference, hence, theNelder-Mead method is selected as a numerical solver for our ML estimatorsince its time execution is the shortest.

    Constrained Localization Algorithm

    We have already introduced the standard ML estimator for the position es-timation. In this part, we propose to adapt this estimator into the context ofWBASN. In this environment, several constraints imposed by the body can pro-vide the ML estimator with extra information to yield more accurate results [5].In comparison to this study, our proposed constraints are more realistic be-cause of paying attention to the practical placement of the sensors on the body.Furthermore, unlike this work, which employs only equality constraints, ourcontribution is to introduce not only the equality ones but also the inequalityones. Without loss of generality, only two sensors T1 and T2 on the left arm (seeFigure 3.2) are analyzed due to the equivalent roles of the sensors on the armsand legs.

    1

    2

    3

    4

    1

    2

    3

    41

    2 3

    4 Reference nodeReference node on the back

    Target node

    Body joint

    Equality constraintx

    y

    z

    Figure 3.2: Constraints in WBASN.

    In case of localization of two target nodes T1 and T2 on the left arm, assumingthat all the ranging measurements are independent and equivalent (i.e. ij =const, i, j) and LOS, the formula (3.7) with Nt = 2 for the 2 nodes T1, T2 andNr = 4 becomes:

    = arg min

    4i=1

    2j=1

    (dij

    (Ri Tj)T (Ri Tj)

    )2, (3.8)

    17

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    where = [T1,T2]T

    =[T

    (x)1 , T

    (y)1 , T

    (z)1 , T

    (x)2 , T

    (y)2 , T

    (z)2

    ]Tis the estimate vector.

    Mathematically, the constraints imposed by the left arm can be formulatedas follows:

    ceq(,J1,J2) =

    [ceq1(,J1)ceq2(,J2)

    ]=

    (T1 J1)T (T1 J1)T1J12

    (T2 J2)T (T2 J2)T2J22

    = 0,(3.9)

    where ceq(,J1,J2) is the matrix that composes of the equality constraints ofthe target nodes which are determined by their positions on the body.

    It is important to remind that the position of the left shoulder joint J1 and

    two fixed distanceT1J1, T2J2 are known a priori whereas the position

    of the left elbow joint J2 is still unknown. Under normal circumstances, whenrelying on the placement of target node in Xsens MVN system [17], we haveto sacrifice the second constraint (i.e. ceq2(,J2) = 0). Therefore, our maincontribution in the constrained algorithm is to gain back this constraint. Byusing analytic geometry, the position of the left elbow J2 can be referred by oneof the left shoulder joint J1 and one of the target node T1 as follows:

    J(x)2 J(x)1T(x)1 J(x)1

    =

    J2J1T1J1J(y)2 J(y)1T(y)1 J(y)1

    =

    J2J1T1J1J(z)2 J(z)1T(z)1 J(z)1

    =

    J2J1T1J1

    , (3.10)

    J2 = J1 +

    J2J1T1J1 (T1 J1) . (3.11)Substituting J2 in (3.9) by (3.11), the constraints now are independent of

    the position of J2. Moreover, due to the unique characteristic of WBASN, wealso have other type of constraint (i.e. inequality constraints). For example, inFigure 3.2, the target node T2 cannot attain positions that are 50 centimetersaway from the corresponding shoulder joint J1. However, we can achieve stricterinequality constraints than this example by using triangle inequality i.e.

    T2J1 T2J2+ J2J1T2T1 T2J2+ J2T1 , (3.12)

    c (,J1,J2) =[c1 (,J1)c2 (,J2)

    ]

    =

    (T2 J1)T (T2 J1)T2J2 J2J1

    (T2 T1)T (T2 T1)T2J2 J2T1

    0, (3.13)

    18

  • 3.2. Relative Localization in WBASN

    where c(,J1,J2) is the matrix that consists of the inequality constraints of thetarget nodes which are identified by their positions on the body.

    In summary, the constrained localization algorithm leads to find the mini-mum of constrained nonlinear multivariable function specified by:

    = arg min

    4i=1

    2j=1

    (dij

    (Ri Tj)T (Ri Tj)

    )2

    subject to

    ceq(,J1,J2) = 0

    c(,J1,J2) 0

    J2 = J1 +

    J2J1T1J1 (T1 J1)

    . (3.14)

    Remind that this optimization problem above is used to estimate the positionof 2 target devices (i.e. = [T1,T2]

    T) on the left arm. For the target nodes on

    other limbs, the process is equivalent. This optimization problem is solvable byKarush-Kuhn-Tucker (KKT) conditions [24].

    Cooperative Localization Algorithm

    In the previous part, the characteristics of this environment (i.e. body con-straints) have been already considered. Consequently, in attempt to improvethe precision as much as possible, the features of WSN are investigated andcooperative techniques are favorable (see also Section 2.3.2). In conventional lo-calizations, unknown-position nodes perform ranging measurements with know-position nodes whereas cooperative ones enable the communications betweenunknown-position sensors [3][19].

    As shown in Figure 3.3, the target (yellow) nodes on the arms make measure-ment with one another to introduce the information redundancies. Our studyalso considers the effect of the cooperative topology or spacial diversity in thequality of the localization. More particularly, three topologies i.e. single-link(SL), quasi-mesh (QM), full-mesh (FM) are evaluated.

    (a) (b)

    (c)

    1

    24

    3

    4 2

    13

    3

    4 2

    1

    Figure 3.3: (a) Single-link cooperative scenario. (b) Quasi-mesh cooperative sce-nario. (c) Full-mesh cooperative scenario.

    19

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    Firstly, considering the single configuration in Figure 3.3(a), only targetnodes on the same arm are paired to perform the ranging measurements. Sensorson different arms are not allowed to exchange packets in this scenario. Thesolution for the ML estimation of the positions (of two sensor T1 and T2) in theequation (3.8) becomes:

    = arg min

    4i=1

    2j=1

    (dij

    RiTj)2+ (dc12 T1T2)2

    , (3.15)where dcij denotes the cooperative measurement between target node i and

    target node j. It is important to remember that dij represents the ordinarynon-cooperative measurement between each pair of reference node i and targetnode j. It can be seen clearly that the likelihood function which is enclosedwithin parentheses in the expression (3.15) has a new coefficient in comparisonwith one of (3.8). This new coefficient expresses the cooperative localization be-tween unknown nodes (i.e. node T1 and node T2) and plays a role in providingthe ML estimator with more information.

    Next, the spacial diversity are fully exploited by quasi-mesh configurationin Figure 3.3(b) and full-mesh one in Figure 3.3(c). In these scenarios, the 4sensors (i.e. T1, T2, T3, and T4) on two arms communicate with one another toform cooperative mesh networks. Consequently, the solution for ML estimationin the equation (3.8) now becomes:

    = arg min

    4i=1

    4j=1

    (dij

    RiTj)2+

    (i,j)T

    (dcij

    TiTj)2 , (3.16)

    where = [T1,T2,T3,T4]T

    and T is the set of cooperative pairs. In caseof quasi-mesh topology, T = {(1, 2), (1, 3), (2, 4), (3, 4)}. Regarding the full-mesh configuration, two extra cooperative ranging measurements are performed,therefore, we provide the other set T = {(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)}.

    Concerning the cooperative approaches, the more cooperative measurements,the more information the ML estimator has. However, it is critical to considerthat there always has a trade-off between extra information and its cost (e.g.over-the-air traffic, power consumption, computational load. . . ). As a result,the topology plays an important role in cooperative techniques. It should giveenough extra cooperative information but not too redundant to increase thecomplexity of the system. It explains why our cooperative sensor networks con-tain maximum 4 nodes. With these configurations, the most complex ML esti-mation is limited to 12-dimensional optimization problem (each target node has3 unknown coordinates on the x-axis, y-axis, and z-axis in Cartesian system).

    Cooperative-cum-constrained Localization Algorithm

    In the previous section, we have investigated the potential performance gain ofboth constrained and cooperative localizations. It is reasonable to merge bothalgorithms into one to fully exploit the body constraints, the spatial diversityand measurement redundancies from WBASN. We call this as cooperative-cum-constrained localization algorithm. For the single-link cooperative topology, the

    20

  • 3.2. Relative Localization in WBASN

    unconstrained optimization problem in (3.15) becomes as follows:

    = arg min

    {[2i=1

    4j=1

    (dij

    RiTj)2]+ (dc12 T1T2)2}

    subject to

    ceq (,J1,J2) = 0

    c (,J1,J2) 0

    J2 = J1 +

    J2J1T1J1 (T1 J1),

    (3.17)

    where estimate vector = [T1,T2]T

    . dij denotes the ranging measurement be-

    tween the reference node i and the target node j whereas dcij is the cooperativeranging measurement between a pair of target devices i and j. ceq (,J1,J2),c (,J1,J2) represent the matrices of equality and inequality constraints re-spectively, as mentioned.

    On the other hand, for quasi-mesh or full-mesh cooperative configuration,this optimization problem is as follows:

    = arg min

    {[4i=1

    4j=1

    (dij

    RiTj)2]+ (i,j)T

    (dcij

    TiTj)2}

    subject to

    ceql (,J1,J2) = 0

    ceqr (,J3,J4) = 0

    cl (,J1,J2) 0cr (,J3,J4) 0

    J2 = J1 +

    J2J1T1J1 (T1 J1)J4 = J3 +

    J4J3T3J3 (T3 J3)

    ,

    (3.18)

    where estimate vector = [T1,T2,T3,T4]T

    . In addition, ceql (,J1,J2) andceqr (,J3,J4) are the matrices of equality constraints in the left and the rightarms, respectively. Similarly, cl (,J1,J2) and cr (,J3,J4) denote the matricesof inequality constraints imposed by the left and the right arms, respectively.

    3.2.3 Simulations and Results

    Performance Criteria

    Before presenting our evaluation framework, it is worth introducing several crite-ria for accuracy. In this section, certain typical measures are defined to evaluatethe performance of the localization algorithms discussed in the previous sections.

    Root Mean Square Error (RMSE) is the root of Mean Square Error(MSE) which is defined as the average of the squares of the distances between

    21

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    the estimator and its true parameter. Its expression can be shown that:

    RMSE()

    =

    E

    { 2} = E{( )T ( )}, (3.19)where is the estimator of the target or true value and =

    [T

    (x)i , T

    (y)i , T

    (z)i

    ]Tin this case.

    The RMSE or MSE is used as a metric to evaluate both mean and varianceof an estimator due to:

    RMSE()

    =

    V()

    +[B()]2

    =

    V()

    +[E()

    ]2, (3.20)

    where V()

    and B()

    are the variance and the bias of the estimator, respec-

    tively.

    Geometric Dilution of Precision (GDOP) is a common metric connectedwith the geometry configuration of the reference nodes relative to the targetnode [8].

    GDOP()

    =

    E

    {[ E ()

    ]T [ E ()

    ]}range

    =

    2x +

    2y +

    2z

    range, (3.21)

    where range denotes the ranging deviation. 2x,

    2y, and

    2z represent the MSEs

    for the x-axis, y-axis, and z-axis estimates, respectively. From this definition,if the GDOP is large, even relative small ranging error can result in seriouspositioning error and vice versa.

    Empirical Cumulative Distribution Function (Empirical CDF) is usedto describe the probability that the random variable D representing the distance

    error (i.e. D = ) will be found to have a value less than or equal to a

    particular value by empirical measure. In our evaluation framework, we are alsointerested in the empirical probability that the distance error is less than orequal to the range cm i.e.

    CDFD(range) = P (D range), (3.22)where P () represents the probability of a random variable.

    Besides, other criteria related to the trade-off between the complexity ofan algorithm and its accuracy are also considered such as the number of re-quired measurements for the first step of the two-step positioning (see againFigure 2.2) and the processing time for the estimator in the second with regardsthe performance improvement.

    Scenario Description and Simulation Parameters

    In this section, we present the framework to evaluate our proposed algorithms.The topology of the reference nodes as well as target nodes of WBASN is de-ployed in a biomechanical model as depicted in Figure 3.4. In this figure, the

    22

  • 3.2. Relative Localization in WBASN

    lengths of the major body segments are determined from [25]. The placementof reference (red) nodes is approximately tetrahedral to obtain the best ge-ometry [8][12] whereas the disposition of the target (yellow) nodes follows incompliance with the Xsens MVN system [17].

    10050

    050

    100100

    500

    50100

    100

    50

    0

    50

    100

    y (cm)x (cm)

    z (cm

    )

    Figure 3.4: Sensor deployment in the biomechanical model for relative local-ization. The reference (red) nodes are attached on the torso while the target(yellow) nodes are mounted on the limbs to capture the motion.

    Concerning the noise model, simplifying the model described in (3.4), themeasurements are added with a ranging error which is independent and Gaus-sianly distributed with a standard deviation range = 5 cm i.e.

    dij N(RiTj , 2ij)

    dcij N(TiTj , 2ij) , (3.23)

    where dij is the measured distance between the reference node i and the target

    node j whereas dcij denotes the estimated cooperative range between a pair of

    target nodes i and j. ij is the standard deviation of the measured distance dijas well as dcij and ij = range,i, j. This deviation is equivalent to the UWBparameters as mentioned in Chapter 2. And moreover, it is in agreement withthe real measurements, which are presented in next chapter.

    Regarding the localization algorithms, a brief description of these is given inTable 3.1. More precisely, our proposed cooperative-cum-constrained algorithmis evaluated in 3 different configurations (i.e. algorithm 7, 8, 9) and it is em-pirically compared with the cases when its components (i.e. constrained andcooperative features) are employed separately (i.e. algorithm 3, 4, 5, 6) and alsowith the conventional localization estimator such as linear LSE estimators (i.e.algorithm 1) [1] and the standard ML one (i.e. algorithm 2).

    23

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    Given ranging deviation range, independent realizations of ranging mea-surements are produced to conduct the simulation of each algorithm describedin Table 3.1 with 10,000 times. As mentioned in the previous section, withoutloss of generality, we only assess the localization performance of the 2 targetnodes T1 and T2 on the left arm in terms of the RMSE, GDOP, empirical CDF,trade-off between the accuracy and the number of measurements/constraints,and the compromise with the processing time.

    Table 3.1: Evaluated relative localization algorithms.

    Algorithm AcronymType of

    estimatorCooperative

    scenarioConstraints

    1 LSE Linear LSE No No2 ML ML No No3 CoopML-SL ML Single-link No4 CoopML-QM ML Quasi-mesh No5 CoopML-FM ML Full-mesh No6 ConML ML No Yes7 CCML-SL ML Single-link Yes8 CCML-QM ML Quasi-mesh Yes9 CCML-FM ML Full-mesh Yes

    Results and Discussion

    Figure 3.5 gives the information about the performance (through RMSE andGDOP) of the localization algorithms described in Table 3.1. It shows the po-sition estimates of the both target nodes T1 and T2. It should be noted thatthe standard deviation of the ranging error is 5 cm. As expected, we observethe clear growths in the performance (i.e. the declines in the RMSE and theGDOP) when comparing the traditional techniques (i.e. algorithms 1 and 2),the cooperative scenarios (i.e. algorithms 3, 4, and 5), the constrained approach(i.e. algorithms 6) and the hybrid ones (i.e. algorithms 7, 8, and 9). Overall,the performance is proportional to the complexity of the algorithm.

    First of all, the simplest one (i.e. LSE or algorithm 1) results in the greatesterror due to the use of the linear estimator. Next, an improvement in accuracyis provided by the standard non-linear LSE (i.e. ML or algorithm 2). Thencooperative scenarios (i.e. algorithms 3, 4, 5 or CoopML-SL, CoopML-QM,CoopML-FM respectively) reduce the inaccuracy gradually. The more compli-cated the cooperative network is, the greater accuracy we achieve. A closerlook at the figure then reveals that the constrained technique (i.e. algorithm6) has different effects on the different target nodes position estimates. Morespecifically, the decease in the GDOP yielded by the body constraints of theT1s position estimates is distinctly superior than that of the T2. Furthermore,in case of the node T2, there exists a slight rise in the RMSE. This is due tothe fact that the position of the node T2 (between the left elbow and the corre-sponding wrist) has more degree of freedom than the node T1 (in the middle ofthe left shoulder and the corresponding elbow). Additionally, it is important toremember that the body constraint relating to the node T2 depends on the T1s

    24

  • 3.2. Relative Localization in WBASN

    position estimate. For this reason, an error in T1s position can be magnified andwould eventually ruin the T2s position estimate. Lastly, the family of hybridalgorithms increases the T2s positioning accuracy moderately but not the T1s.The detailed explanation will be provided later.

    1 2 3 4 5 6 7 8 90

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    RMSE of node T1RMSE of node T2GDOP of node T1GDOP of node T2

    Figure 3.5: Relative localization RMSE and GDOP in case of range = 5 cm.

    Before analyzing these statistics of the errors, we compare our results withthose of other existing works using constrained localization [4][5] in term of thedecrease in RMSE. Particularly, the constrained ML in [5] results in a relativedrop in average RMSE per node of 36.2 %57.3 % (depending on the nodesposition) in comparison with that of the standard ML but this work is limitedto 2D positioning. On the other hand, this enhancement is only maximum 17 %in [4] on account of the 3D localization and the reduction in the number ofconstraints. In contrast, our constrained ML produces the relative enhancementof 32.4 % over the ML. Moreover, our hybrid CCML can give more performance(maximum relative improvement of 42.6 %).

    Figure 3.6 represents the empirical CDF of the distance error in the T1sposition estimates. From this figure, similarly, the family of conventional algo-rithms (i.e. LSE and ML in blue and red, respectively) gives the poorest per-formance. The family of cooperative scenarios (i.e. CoopML-SL, CoopML-QM,CoopML-FM in brown, pink and light blue, respectively) gains somewhat accu-racy compared with the standard ML but noticeable improvement with the LSEwhile the hybrid localization schemes (i.e. CCML-SL, CCML-QM, CCML-FMin yellow, orange and light green, respectively) and the constrained scheme (i.e.ConML in black) outperform the others. Specifically, let consider the probabilityof distance error that is less than or equal to range or 5 cm. The performance isthe least for the LSE (i.e. approximately 6.8 %), then increases greatly to about17 % for the ML (relative improvement by 151 %) due to the account for thestatistics of noise sources. From there, it however rises slightly to about 20.5 %,

    25

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    21.1 %, and 23.4 % (relative improvements by 20.6 %, 24.1 % and 37.7 %) us-ing CoopML-SL, CoopML-QM, and CoopML-FM respectively. This could beexplained by the fact that the set of cooperative measurements provides theML with some extra information but the trade-off is that this additional datamay contain errors. Overall, these increases brought by cooperative approachesare still noticeable. Regarding the ConML, there is a dramatic enhancement toaround 67.1 % (relative improvement by about 300 % against the ML). This isdue to the fact that the equality constraint gives additional but exact informa-tion by limiting the possible results to the spherical surface so the ML estimatoronly searches the extrema on this. As a result, the body constraints lead to suchan excellent performance gain. Concerning our proposed CCML algorithm, it isapplied in 3 kinds of cooperative topologies (i.e. CCML-SL, CCML-QM, andCCML-FM) and produces the best performance as expected. However, the gapsin performance between these scenarios and the ConML appear fairly small.These are due to the constraints, which are built by the known a priori in-formation, are powerful here. On the other hand, as discussed, the cooperativeinformation with less reliability does not help much to improve the accuracyin this case. Nonetheless, under the circumstances, the constraints may becomeless reliable that we study next or are inexistent that is examined in the nextsection about absolution localization. In these conditions, cooperative scenariospromise some enhancement of accuracy.

    0 5 10 15 20 250

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    LSEMLCoopMLSLCoopMLQMCoopMLFMConMLCCMLSLCCMLQMCCMLFM

    Figure 3.6: Empirical CDF plot of the localization error of the node T1s positionestimate in case of range = 5 cm.

    Similarly, Figure 3.7 also compares the performance of the studied algorithmsthrough the empirical CDF of the distance error in node T2s position estimate(i.e. the node between the left elbow and its corresponding wrist). As can be seenfrom this plot, the ConML, the CoopML-QM, and the CoopML-FM producethe performance as much as each another. In other words, in this case, the gaps

    26

  • 3.2. Relative Localization in WBASN

    0 5 10 15 20 250

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    LSEMLCoopMLSLCoopMLQMCoopMLFMConMLCCMLSLCCMLQMCCMLFM

    Figure 3.7: Empirical CDF plot of the localization error of the node T2s positionestimate in case of range = 5 cm.

    between the ConML and the family of cooperative approaches are narrower.Quantitatively, the probabilities of distance error that is less than or equal torange for the ConML, the CoopML-QM, and the CoopML-FM reach 25 %,20.2 %, and 21.2 % respectively. These are due to the effect of the uncertaintyin T1position estimate on the constraint relating to the node T2, as discussed.The constrained approach loses rather its efficiency in this case. Despite this,the CCML can help to partially get back the localization performance.

    Figure 3.8 shows how the information redundancies (i.e. the number of con-straints and/or cooperative measurements) impact the localization performancewhich is evaluated by the probability of the distance error which is less than orequal to range cm (i.e. 5 cm) in the estimates of 4 nodes T1, T2, T3 and T4 on2 arms with 10,000 independent trials. Overall, we can observe that the moreinformation provided, the more gain in accuracy we achieve. Furthermore, thebody constraints produce much improved precision than the cooperative mea-surements as discussed. We also observe that in this case, it is not necessary toemploy the most complicated cooperative network (i.e. full-mesh cooperation)since it does not give much performance than the simpler ones (i.e. quasi-meshor single-link topology).

    The extra information can promise the decrease in localization error butit also causes the increase in the complexity of the estimator. The trade-offbetween the processing time and the accuracy performance is illustrated in Fig-ure 3.9. The accuracy is assessed in the same way as the previous simulation i.e.the probability of the distance error which is less than or equal to range cm (i.e.5 cm) in the estimates of 4 nodes T1, T2, T3 and T4 on 2 arms. The processingtimes are recorded in case the algorithms are implemented by MATLAB R2012b,running single thread on a Intel Xeon E5-1603 CPU with 8 Gb of RAM and

    27

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    64-bit Windows 7 OS. Efficient results are likely to be platform, and MATLABversion, dependent [26]. These algorithms are simulated with 10,000 trials to ob-tain the average results. The LSE (i.e. algorithm 1) has the shortest processingtime (around 0.26 ms/estimate) since it employs the linear estimator. While theothers rely on the non-linear ones, these processing times are greatly multiplied.Particularly, the ML (i.e. algorithm 2), which is the 3-dimensional optimization,spends approximately 16 ms/estimate on calculation. On the other hand, theCoopML-SL (i.e. algorithm 3) estimates two target nodes positions jointly lead-ing to the 6-dimensional optimization, hence, the computation times of theseare grown considerately to around 70 ms. Alternatively, the CoopML-QM (i.e.algorithm 4) and the CoopML-FM (i.e. algorithm 5) calculate concurrently the4 target nodes positions tending to the 12-dimensional optimization. As a con-sequence, their processing times reach the peak of about 170 ms. Concerningthe ConML (i.e. algorithm 6) and the hybrid approaches (i.e. algorithm 7, 8,and 9), their computing times are as much as each another i.e. about 110 mswhich is less than those of the cooperative techniques. This is due to the factthat the constraints limit the domain of the possible extremums leading to fasterconvergence.

    1 2 3 4 5 6 7 8 90

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    Figure 3.8: Trade-off between the accuracy and the number of cooperative mea-surements/constraints in case of range = 5 cm.

    Overall, in order to improve the performance, the non-linear estimator withconstraints and/or cooperative information must be employed. For this reason,the increase in processing time is foreseen and inevitable. Since the algorithmsare implemented using MATLAB and the programs are not optimized, the pro-cessing times are rather high (up to 180 ms). However, there exist countlessstudies which benchmark the processing speech of MATLAB and other generalpurpose programming languages e.g. C, C++, Fortran. . . All of them arrive atconclusion that MATLAB is at least 10 times slower than C++. Moreover, it is

    28

  • 3.3. Absolute Localization in WBASN

    certainly possible to obtain 10, 100, or even 1000 times speedup [26][27]. Due totime constraint, we do not optimize the programs and rewrite these in C++ toreduce the processing times yet our results can provide a look at the complexityand the possibility of implementations of the algorithms.

    1 2 3 4 5 6 7 8 90

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    Figure 3.9: Trade-off between the accuracy and the processing time in case ofrange = 5 cm.

    3.3 Absolute Localization in WBASN

    3.3.1 Absolute Localization System Model

    This section addresses on the deployment of external elements of infrastruc-ture, set as fixed reference nodes. This set of devices defines the GCS that isemployed to represent on-body nodes absolute positions. In comparison withthe GCS, the LCS in relative localization is in movement with the body whilethe GCS is placed at known positions in the environment to form the referenceinfrastructure [4].

    Figure 3.10 shows a typical deployment scenario for absolute localization. Inthis case, the group of target (yellow) nodes (i.e. {Ti}Nti=1, Nt = 10) is mountedon the body in exactly the same way as in relative localization. Conversely,the placement of reference (red) nodes (i.e. {Ri}Nri=1, Nr = 4) is moved outof the body. For this reason, the ranging measurements between each pair ofoff-body (reference) node and on-body (target) node are considered as off-bodycommunications to distinguish from the on-body ones which both ranging de-vices are mounted on body as in the relative localization [21]. The off-bodylinks are clearly different from the on-body ones in terms of transmission range,channel characterization, error model. . . However, in this section, we do not dig

    29

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    deep into these issues and only employ a simple error model for both of thesecommunications in order to evaluate the localization algorithms.

    1

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    Figure 3.10: Absolute localization system for WBASN.

    Alternatively, one claimed advantage of the absolute scenario is the flexibilityto place the reference nodes in the best topology to gain the performance sincethe choices are very limited in relative deployment. An important point to re-member is that the best arrangement of reference sensors refers to a tetra-hedron [8][12]. However, that is much better to have the target nodes whosepositions are inside this tetrahedron. These unique topologies can only found inthe absolute scenarios.

    Similar to the relative scenario, as the ranging measurements are derivedby the IR-UWB ToA information, given the position {Ri}Nri=1, the positionestimator is able to compute the location of the target nodes, i.e. {Ti}Nti=1.

    3.3.2 Absolute Localization Algorithms

    In the context of absolute localization, the reference system is implementedout of the body to define the GCS. It enables the use of off-body rangingmeasurements which are the peer-to-peer communications between each on-body target sensor and each external fixed reference node. Since the referencesystem is no longer body-strapped, the information about the body joints/bends

    (i.e. {Ji}Nji=1), which is mandatory to set up the constraints, is not available.Therefore, the constrained localization algorithm cannot be put into practice.In spite of this, the cooperative approaches are still compatible. Besides the off-body communications, enabling the on-body links as cooperative ones yields afew information redundancies to enrich the localization performance. Regardingthe cooperative algorithms, we consider the same scenarios as in Section 3.2.2for relative localization i.e. single-link, quasi-mesh, and full-mesh cooperativenetworks.

    30

  • 3.3. Absolute Localization in WBASN

    3.3.3 Simulations and Results

    Scenario Description and Simulation Parameters

    In our evaluation framework, the deployment of the absolute localization isillustrated in Figure 3.11. The biomechanical model and the disposition of thetarget sensors are the same as in the relative localization whereas the set ofreference nodes is external to the body and placed on the tetrahedron. By which,it means that there are 2 types of ranging measurements, i.e. on-body ones andoff-body ones. For simplification purposes, we still rely on the same error modeland parameters as in Section 3.2.3 about relative localization for both typesof measurements. Particularly, the ranging measurements follow the model inexpression (3.23) with a standard deviation of 5 cm.

    050

    100150

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    y (cm)x (cm)

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    )

    Figure 3.11: Sensor deployment in the biomechanical model for absolute local-ization. The reference (red) nodes are installed at known positions in the envi-ronment while the target (yellow) nodes are mounted on the limbs to capturethe motion.

    Concerning the localization algorithms, Table 3.2 provides the short descrip-tion about which are evaluated. It should be noted that the constrained schemesare not considered in this context. As a result, every target node has the equiv-alent role. Without loss of generality, the T1s position estimate is examined.

    Results and Discussion

    Figure 3.12 shows the performance (by means of the RMSE and GDOP) of thelocalization algorithms (i.e. in Table 3.2) in the T1s position estimate. As re-gards the RMSE, it remains stable for the first 2 algorithms (i.e. LSE and ML)and shrinks steadily in the cooperative scenarios (i.e. CoopML-SL, CoopML-QM, CoopML-FM or algorithms 3, 4, 5 respectively). We notice that there isalmost no difference in performance between the LSE (RMSE of 7.69 cm) and

    31

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    Table 3.2: Evaluated absolute localization algorithms.

    Algorithm AcronymType of

    estimatorCooperative

    scenarioConstraints

    1 LSE Linear LSE No No2 ML ML No No3 CoopML-SL ML Single-link No4 CoopML-QM ML Quasi-mesh No5 CoopML-FM ML Full-mesh No

    the ML (RMSE of 7.65 cm) thanks to the geometry of the target nodes relativeto the reference nodes. It is important to remember that in relative localization,the big gap in performance between the LSE and the ML caused by the dispo-sition of the target devices is external to the tetrahedron formed by referencesensors. Alternatively, the RMSE is also inversely proportional to the infor-mation redundancies. Specifically, the CoopML-FM produces the lowest error(RMSE of 6.42 cm). Then it is the CoopML-QM (RMSE of 6.78 cm) and finallythe CoopML-SL (RMSE of 7.2 cm). However, the decrease in GDOP is morenoticeable. The GDOP reaches its peak of 1.548 in case of LSE, then reducesvery slightly to 1.54 for the ML. Then, it experiences a steady decrease for thecooperative scenarios to 1.287 for CoopML-FM leading to relative improvementof 16.86 % in comparison with the worst case (i.e. LSE).

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    Figure 3.12: Absolute localization RMSE and GDOP of the node T1s estimatein case of range = 5 cm.

    Figure 3.13 represents the empirical CDF of the distance error in the T1sposition estimate. The performance results, which have already derived from the

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  • 3.3. Absolute Localization in WBASN

    0 5 10 150

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    Figure 3.13: Empirical CDF plot of the localization error of the node T1s posi-tion estimate in case of range = 5 cm.

    last figure (i.e. Figure 3.13), are verified once again. For example, let considerthe probability that the distance error is less than or equal to range or 5 cm,it hits the bottom of 83.41 % at the LSE (in blue) then increases modestly to83.85 % for the ML (in green). After that, the enhancements are more noticeablefor the cooperative techniques i.e. 87.76 %, 91.01 %, and 93.33 % correspondingto the CoopML-SL, CoopML-QM, and CoopML-FM respectively.

    Similar to the analyse of the relative localization, the effect of the informa-tion redundancies (i.e. the number of cooperative links) on the accuracy (bymeans of the probability of the distance error which is less than or equal torange or 5 cm in the estimates of 4 nodes on the arms with 10,000 trials)is shown in Figure 3.14. Expectedly, the wealth of cooperative information isproportional to the localization performance.

    Last but not least, the trade-off between the processing time and the accu-racy performance is illustrated in Figure 3.15. The means of evaluation is exactlythe same as that in the recent discussion or in the relative scheme. The resultsare also similar. The non-linear LSE (i.e. algorithm 1) has the least processingtime i.e. around 0.3 ms corresponding to the probability of 26.92 %. The ML(i.e. algorithm 2) appears not to be efficient in this scenario due to the executiontime of about 28.4 ms (about 100 times larger than that of LSE) for the modestimprovement of the probability to 27.5 %. Followed by the CoopML-SL (i.e.algorithm 3), which estimates the couple of target nodes jointly, the processingtime is about half as much again as that of the ML (i.e. roughly 443 ms) inorder to obtain the probability of 31.48 %. The CoopML-QM and CoopML-FM,however, calculate the positions of the 4 nodes simultaneously, hence, requiremore time (i.e. approximately 88.4 ms for both) to reach the probability of35.51 % (CoopML-QM) and 39.97 % (CoopML-FM). Overall, it seems that theprice for the accuracy enhancement in absolute localization using cooperative

    33

  • CHAPTER 3. COOPERATIVE-CUM-CONSTRAINED ALGORITHM

    approach is rather soaring. However, as mentioned, the millisecond processingtime in MATLAB can be reduced to microsecond one in C++ language thatis not a matter for real localization systems. Moreover, when the movements ofthe human body cause any target node to be outside the tetrahedron formed bythe set of reference nodes, the accuracy is degraded as in the relative scenario.Therefore, it is worth implementing the cooperative localization in this case.

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    Figure 3.14: Trade-off between the accuracy and the number of cooperativemeasurements in case of range = 5 cm.

    3.4 Conclusions

    In this chapter, the WBASN-specific localization algorithms have been devel-oped for both relative scheme and absolute scheme. The characteristics of theWBASN are fully exploited to obtain the performance gain i.e. the well-knowncooperative techniques in WSN and the constraints in the body area. As a result,the combined localization algorithms are analyzed.

    In relative localization, we have observed the critical improvements in ac-curacy brought by the constrained algorithm. Although its efficiency dependson the nodes degree of freedom, it can be applied jointly with the cooperativetechniques to minimize the errors.

    On the other hand, the absolute scheme can only take advantage of thecooperative methods. In spite of this, the flexibility of the placement of thereference nodes can provide the incredible performance gain.

    Comparing these 2 localization schemes, we can observe that in case of em-ploying the same localization algorithm, the absolute scheme gives better perfor-mance than the relative one due to the geometry of the target nodes relative tothe reference nodes. However, the constrained and the hybrid techniques, which

    34

  • 3.4. Conclusions

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    Figure 3.15: Trade-off between the accuracy and the processing time in case ofrange = 5 cm.

    are unique to relative scenario, outperforms the all techniques used in absolutescheme.

    35

  • Chapter 4

    Experiments

    This chapter addresses the real experiments that characterize the IEEE 802.15.4-2011 UWB technology, its reliability and range capability using DecaWavesIR-UWB platforms. Then we partially discuss the performance of our proposedalgorithms with real ranging measurements.

    4.1 Evaluation Platform

    Our empirical work is performed using an evaluation kit EVK1000, which in-cludes a pair of FCC-compliant IR-UWB platforms (EVB1000) developed byDecaWave, a fabless semiconductor company in Dublin, Ireland [6]. Integratinga single radio frequency (RF) localization chip DW1000 in support of the IEEE802.15.4 UWB PHY standard and a pre-installed two-way ranging application,this platform (i.e. EVB1000) provides peer-to-peer ranging measurements witha precision of 10 cm in high multipath, highly reflective environments. Thus, itenables to develop the Real Time Location System (RTLS). Additionally, oper-ating in 6 RF bands from 3.5 GHz to 6.5 GHz and relying on coherent receiver,this platform supports the data rate of up to 6.8 Mbps and the coverage of upto 290 m. Concerning ToA estimation, the leading path arrival time is detectedfrom the channel impulse response data stored in the register within the timeresolution of sub-nanosecond. Moreover, low power consumption of DW1000(typically from 31 mA in transmit mode and from 64 mA in receive mode) iscompatible with the context of WBASN [6][28][29][30].

    4.2 Experimental Configurations

    4.2.1 Use-case Configuration

    The DecaWaves platform supports a variety of operational design choices forseveral applications (i.e. RTLS, data transfer. . . ). However, in our experimentframework, we focus on its use in RTLS. The configuration of the platforminvolves various parameters including channel (defined by the center frequencyand the bandwidth), data rate, preamble length, Pulse Repetition Frequency(PRF). . . Parameters settings and details are described in [28]. Alternatively, dueto the effect of the signal bandwidth on the CRLB of the ToA estimate expressed

    37

  • CHAPTER 4. EXPERIMENTS

    Figure 4.1: DecaWaves IR-UWB EVK1000 kit with its package.

    in the inequality (2.2), the largest bandwidth supported by the platform isemployed. Precisely, the 900 MHz frequency band with its center frequency of6.5 GHz is configured for the experiments. Concerning other parameters, briefly,we rely on the 2 operational modes recommended by [29] as shown in Table 4.1.

    Table 4.1: Configuration modes.

    ModePreamble(Symbols)

    Non StdSDF

    PRF(MHz)

    DataRate

    Remarks

    1 1024 Yes 64 110 kbpsLong Range,Low Density

    2 128 No 64 6.8 MbpsShort Range,High Density

    These operational choices above follow several considerations recommendedby DecaWave to developed the RTLS [28][29][30]. We are interested in evaluatingthe platforms ranging capability in both long distance and short one. Lowdata rate (110 kbps) is preferred for long range communication while shortoperating range supports high data rate (6.8 Mbps). The highest PRF (64 MHz)is employed due to the most accuracy on the first path timestamp. Alternatively,for long range, long preamble code (1024 symbols) is used to achieve this range.However, short range communication, which usually employs high data rate,does not have to send long preamble code to save time and power. As a result,short preamble code (128 symbols) is configured.

    4.2.2 Platform Calibration

    While recording the ranging measurements using time-based approaches, themost important and also challenging problem is to perform the antenna delaycalibration. The objective of this work is to ensure accurate range measurements,

    38

  • 4.2. Experimental Configurations

    which depend on the precise estimation of timestamps. The notion of antennadelay is introduced to offer the facility to compensate for the delays causedby the Printed Circuit Board (PCB), external components, antenna as wellas internal delays [28][30]. Before going further, it is necessary to analyze theoperation of the DecaWaves two-way ranging protocol, which is different fromone in Figure 2.3.

    This platform uses a set of 3 packets to complete the RToA measurementsfrom which the range is implied as pictured in Figure 4.2. The operation of thisprotocol is as follows [9][30]:

    The target node initializes a ranging measurement by sending a poll packetcontaining its ID at time T1.

    The reference node then receives the this packet at time T2 and replieswith a response packet at time T3.

    The target node latterly receives the response packet at time T4 and thenat time T5 sends back a final packet including its ID, the timestamps ofitself (i.e T5), and of the previous packets (i.e T1, T4).

    The reference node receives this packet at time T6