A Cognitive Radio Tracking System for Indoor Environments

Transcription

1 A Cognitive Radio Tracking System for Indoor Environments by Azadeh Kushki A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Electrical & Computer Engineering University of Toronto c Copyright by Azadeh Kushki, 2008

2 Abstract A Cognitive Radio Tracking System for Indoor Environments Azadeh Kushki Doctor of Philosophy Graduate Department of Electrical & Computer Engineering University of Toronto 2008 Advances in wireless communication have enabled mobility of personal computing devices equipped with sensing and computing capabilities. This has motivated the development of location-based services (LBS) that are implemented on top of existing communication infrastructures to cater to changing user contexts. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This thesis introduces a cognitive dynamic system for tracking the position of mobile users using received signal strength (RSS) in Wireless Local Area Networks (WLAN). The main challenge in WLAN positioning is the unpredictable nature of the RSS-position relationship. Existing system rely on a set of training samples collected at a set of anchor points with known positions in the environment to characterize this relationship. The first contribution of this thesis is the use of nonparametric kernel density estimation for minimum mean square error positioning using the RSS training data. This formulation enables the rigorous study of state-space filtering in the context of WLAN positioning. The outcome is the Nonparametric Information (NI) filter, a novel recursive position estimator that incorporates both RSS measurements and a dynamic model of pedestrian motion during estimation. In contrast to traditional Kalman filtering approaches, the NI filter does not ii

3 require the explicit knowledge of RSS-position relationship and is therefore well-suited for the WLAN positioning problem. The use of the dynamic motion model by the NI filter leads to the design of a cognitive dynamic tracking system. This design harnesses the benefits of feedback and position predictions from the filter to guide the selection of anchor points and radio sensors used during estimation. Experimental results using real measurement from an office environment demonstrate the effectiveness of proactive determination of sensing and estimation parameters in mitigating difficulties that arise due to the unpredictable nature of the indoor radio environment. In particular, the results indicate that the proposed cognitive design achieves an improvement of 3.19m (56%) in positioning error relative to memoryless positioning alone. iii

4 Acknowledgements I would like to express my deep gratitude to my supervisors, Professor Plataniotis and Professor Venetsanopoulos, for their guidance and advice. I am thankful for all they have done to support me during these many years. I am also indebted to my committee members, Professor Hatzinakos and Professor Aarabi, for their enthusiasm and insightful comments. I would like to thank the Natural Science and Engineering Research Council of Canada (NSERC) and the Rogers family for their generosity in supporting my work. Lastly, my infinite thanks go to my family for their never-ending love and support. This work is dedicated to them. iv

5 Contents Abstract ii Acknowledgements iv 1 Introduction Motivation Ethics of Location Computing WLAN Positioning Problem Statement & Definitions Positioning Architectures Key Technical Challenges Thesis Contributions Thesis Organization Background & Related Work RSS-Position Dependency Radio Propagation Modeling Fingerprinting-Based Methods Region of Interest Determination Access Point Selection v

6 2.4 Memoryless Positioning K-Nearest Neighbour Technique Probabilistic Techniques Pattern Recognition Techniques Dynamic Positioning (Tracking) Chapter Summary Experimental Setup General Setup The Building Measurement Apparatus Anchor Point Placement Mobility Scenarios Stationary User The Environment Data Sets Ground Truth Sources of Measurement Errors Figure of Merit Mobile User The Environment Data Sets Ground Truth Sources of Measurement Errors Figure of Merit Chapter Summary vi

7 4 Memoryless Positioning The Memoryless Positioning Problem Method 1: Likelihood Density Estimation Kernel Density Estimation The MMSE Estimate Method 2: Joint Density Estimation Product Kernel Density Estimation MMSE Estimate Comments on the Proposed Estimators The Nadaraya-Watson Estimator Complexity Experimental Evaluation Sensitivity Analysis Performance Benchmarks Performance Comparison Chapter Summary The Nonparametric Information Filter The Bayesian Tracking Problem The Nonparametric Information Filter State Prediction (Model Contribution) Measurement Update (Spatial Processing) Prior Knowledge State Estimation Comments on the NI Filter Interpretation as an Information Filter vii

8 5.3.2 Suboptimality Experimental Evaluation Sensitivity Analysis Performance Benchmarks Performance Comparison Chapter Summary A Cognitive Design Overview of the Cognitive Tracking System Anchor Point Selection Access Point Selection Bhattacharyya Distance Fisher Criterion Complexity Outlier Mitigation Experimental Evaluation Sensitivity Analysis Performance Benchmarks Performance Comparison Tracking Examples Chapter Summary Conclusions Summary of Contributions Directions for Future Work viii

9 A Complexity Analysis: Memoryless Positioning 120 A.1 K-Nearest Neighbour A.2 Histogram A.3 Likelihood Density Estimation A.4 Joint Density Estimation B Derivation of the NI Filter 123 Bibliography 126 ix

10 List of Figures 1.1 The problem setup Overview of architectural design of WLAN positioning systems Example of RSS measurements over time at a fixed location Overview of RSS-based WLAN positioning Map of the experimentation environment. Anchor points are depicted as black circles and arrows indicate the orientation of the laptop during training Distribution of the number of APs over the experimental area (stationary user) Example of spatial RSS distributions. RSS values are averaged over 100 samples for each anchor points Location of the example measurement points Map of the experimental area. Black dots represent anchor points and arrows indicate the orientation of laptop during training Distribution of the number of APs over the experimental area (mobile user) Examples of RSS histograms for different APs at two different anchor points (320 RSS samples are used to build each histogram) x

11 4.2 Examples of kernel density estimates of RSS distributions for different APs at two different anchor points (320 RSS samples are used to build each estimate). Results are reported for σ ri = σr i, σ ri = 2σr i (over smoothing), and σ ri = 0.5σr i (under smoothing) Outline of the positioning algorithm using likelihood density estimation Outline of the positioning algorithm using joint density estimation Comparison of the two proposed MMSE estimators Effect of kernel bandwidth on ARMSE Effect of RSS representative choice on ARMSE (joint kernel density estimation) Effect of the number of training sample on ARMSE Effect of number of neighbors (K) on the performance of KNN Effect of bin width on ARMSE for the histogram Effect of number of neighbors (K) on the performance of the histogram-based method Cumulative distribution of the l 2 norm of positioning error The Nonparametric Information (NI) filter Overview of NI filter Structure of the proposed filter Effect of RSS kernel bandwidth on ARMSE Effect of state kernel bandwidth on ARMSE Effect of system parameters on ARMSE Effect of filter initial conditions on ARMSE Outline of the Kalman filtering algorithm Outline of the regularized particle filtering algorithm. The experiments use N particles = xi

12 5.10 Effect of system parameters on ARMSE for the NI, Kalman, and particle filters Cumulative distribution of the l 2 norm of positioning error The proposed cognitive system Effect of the size of ROI (g) on ARMSE Effect of outlier mitigation threshold η on AMRSE (test data sets) Effect of AP selection strategy on AMRSE (test data sets) Example track results Example of filter operation xii

13 List of Tables 3.1 RSS sample statistics for five example locations and two APs Some well-known univariate kernel functions Computational and storage complexity of the proposed methods compared to the state-of-the-art. Parameters L, N, n, d, are b are the number of APs, number of anchor points, number of time samples per anchor point, and the number of histogram bins Positioning error statistics for KNN, histogram, and the proposed estimators (stationary user) Positioning error statistics for KNN, histogram, and the proposed estimators (mobile user) Summary of experimental results for memoryless positioning. Results are reported in units of meters ARMSE for positioning using the memoryless estimator and NI filter. Kernel bandwidth value of σ = 0.4σ is used ARMSE results for each motion scenario. The results are obtained using σ r = 0.4σr, σ p = σp, and q = Positioning error statistics for the memoryless estimator compared to those of Kalman, particle, and NI filters (stationary user) xiii

14 5.4 Positioning error statistics for the memoryless estimator compared to those of Kalman, particle, and NI filters (mobile user) Summary of experimental results for dynamic positioning. Results are reported in units of meters Computational and storage complexity for four AP selection methods: DM (Bhattacharyya distance σ m i = σ n i, n, m, i), B (Bhattacharyya distance), F (Fisher criterion), S (strongest signal power) Positioning error statistics for memoryless KDE estimator, the NI filter, the cognitive anchor point and AP selection methods (stationary user) (g = 4 for cognitive NI filter and g = 2 for Fisher AP selection) Positioning error statistics for memoryless KDE estimator, the NI filter, the cognitive anchor point and AP selection methods (mobile user)(g = 4 for cognitive NI filter and g = 3.5 for Fisher AP selection) Summary of experimental results for the cognitive design. Results are reported in units of meters xiv

15 List of Commonly Used Symbols d number of APs used in positioning (in this thesis d = 3) F(p i ) I d k L L N N(k) n p p i r a (k) r(k) r i (k) R σ r σ p τ F(p i ) [r i (1)... r i (n)] is the fingerprint matrix for anchor point p i the d d identity matrix Index of time during the on-line operation of system Total number APs available in the environment Number strongest APs used during AP selection Number of anchor points used for location fingerprinting Number of anchor points inside the region of interest at time k Number of training RSS samples collected over time at p i location of mobile client in 2D Cartesian coordinates, p= [p x p y ] T p i = [p x p y ] T specifies the Cartesian coordinates of the i th anchor point RSS value observed from the a th AP at time k Vector of observed RSS values from L APs at time k, r = [r 1 (k),..., r L (k)] vector of RSS values r at location p i and time k R {(p i, F(p i )) i = 1... N} is the radio map KDE bandwidth parameter for RSS measurements KDE bandwidth parameter for position components Index of time during the training phase xv

16 List of Commoly Used Abbreviations AP ARMSE KDE LBS MMSE NI filter ROI RSS WLAN Access Point Average Root Mean Square Error Kernel Density Estimate Location-Based Services Minimum Mean Square Error Nonparametric Information filter Region of Interest Received Signal Strength Wireless Local Area Network xvi

17 Chapter 1 Introduction 1.1 Motivation The problem of positioning and tracking has been studied extensively over the past five decades. Traditionally, the scope of these studies was limited to military and civilian target tracking and navigation because of sensing and computational constraints. More recently, advances in wireless communication have enabled mobility of personal computing devices, such as laptops and cellular phones. The increasing embedded sensing and computing capabilities of these mobile devices, has sparked new location-based services (LBS) and applications that cater to changing user contexts [1]. An important application area for LBS is network management [2, 3] since communication and resource needs of wireless mobile devices are dependent on the physical location of the device. In addition, LBS include value-added services offered on top of existing communication infrastructures including navigation [4], location-specific information delivery [5] and advertising [6]. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This motivates the renewed interest in development of effective positioning and tracking solutions. For the purposes of this thesis, the objective of a posi- 1

18 1.1. Motivation 2 tioning system is to determine the physical coordinates of a wireless mobile device carried by a human user. Well-known examples of existing positioning systems are the Global Positioning System (GPS) and cellular network based systems [4, 7] used for navigation and location-based emergency and commercial services. Application of LBS is not limited to outdoor settings. In fact, the wide availability and ubiquity of indoor wireless networks, such as IEEE , has inspired the delivery of location-specific network functions and higher-level location-aware applications in indoor environments as well. Examples of indoor LBS include location-based network access, management, and security [2, 3, 8], automatic resource assignment, health care [2, 9], locationsensitive information delivery [10], and context-awareness [11]. Unfortunately, the positioning accuracy provided by existing cellular-based methods is insufficient for many indoor applications 1 [13]. Moreover, the coverage of inexpensive and embedded GPS devices is limited in indoor environments [1,14]. Indoor positioning systems, therefore, rely on other sensing modalities [1, 15] such as proximity sensors, infrared [16], radio frequency (RF) and ultrasonic badges [17, 18], and visual sensors [19]. These modes of positioning, however, require installation of infrastructure which can lead to hardware and labor overheads in large environments and pervasive deployments. This thesis focuses on positioning based on Wireless Local Area Network (WLAN) radio signals [20, 21] as an inexpensive solution for indoor environments. In addition to costeffectiveness, WLAN positioning allows for a terminal-based implementation that is essential to preservation of users privacy. To further elaborate on this point, the next section provides a brief note on ethics of location computing. 1 For example, the Federal Communications Commission requires wireless providers to report location information with an accuracy of meters depending on the technology used [12].

19 1.2. Ethics of Location Computing Ethics of Location Computing Positioning techniques developed in this thesis are intended to serve as enabling technologies for Location-Based Services that aim to improve the quality of experience for their users. Since positioning involves direct monitoring of humans, it is imperative to ensure that this technology does not result in the infringement of civil liberties and rights [22]. To this end, a positioning system must entail two features Consent: Positioning must be initiated and terminated with the consent of users. Privacy: Location information collected over a period of time may be used to infer personal behavioural patterns [23]. It is therefore important that this information is computed, stored, communicated in a secure manner. 1.3 WLAN Positioning WLAN 2 positioning is the process of determining the physical coordinates of mobile network devices, such as laptops or personal digital assistants, based on observing radio signals exchanged between the device and Wireless Local Area Network (WLAN) access points (APs). With accuracies in the range of 1-5 meters, WLAN positioning systems can be used to infer location information represented either symbolically (e.g., room 4157) or numerically as two or three dimensional Cartesian coordinates. WLAN positioning is favored over other indoor positioning technologies for three reasons [24, 25]: Cost effectiveness: WLAN positioning exploits the dependency between the location of a mobile device and characteristics of radio signals exchanged between the device and 2 The term WLAN refers to IEEE802.11b/g Wireless Local Area Networks in this thesis.

20 1.3. WLAN Positioning 4 a set of physically distributed WLAN APs. Signal characteristics used for positioning include Time of Arrival (ToA) [26], Time Difference of Arrival (TDoA), Angle of Arrival (AoA) [27], and Received Signal Strength (RSS). The feature of choice in WLAN positioning systems is RSS because it can be obtained directly from Network Interface Cards (NIC) available on most hand-held computing devices. Therefore, RSS-based positioning algorithms can be implemented on top of existing WLAN infrastructures without the need for any additional hardware. This makes WLAN positioning a particularly cost effective solution for offering value-added location specific services in commercial and residential indoor environments. Scalability: RSS-based WLAN positioning systems offer scalability in two respects: 1) cost of required infrastructure and hardware, and 2) number of mobile devices subscribing to positioning services. Hardware scalability is due to the wide deployment of WLAN APs in commercial and residential environments as well as the increasing availability of IEEE capabilities on personal devices such as phones and ipods. Scalability in the number of users is achieved in terminal-based positioning solutions since each mobile device is responsible for performing its own sensing and computations. Moreover, RSS measurements include MAC addresses of APs as well as that of mobile devices. This eliminates the overhead of data association techniques used in other positioning modalities including visual surveillance. Consent & privacy: WLAN positioning adheres to ethics of location computing outlined in Section 1.2 since in contrast to visual surveillance all sensing operations require the cooperation of the mobile device. Moreover, in terminal-based positioning, users must initiate positioning operations (for example, by starting a software program on the device). They can also choose to terminate positioning services by shutting off wireless communications with the infrastructure. Lastly, since positioning operations

21 1.4. Problem Statement & Definitions 5 can be fully implemented on mobile clients, no invasive sensing, processing, and storage is required in WLAN positioning. Consequently, the need for implementation of additional security measures to protect position estimates is eliminated as no information is communicated over wireless links. Due to the above advantages, RSS-based WLAN positioning systems can be implemented over large areas to serve many mobile devices simultaneously in a scalable and cost-effective manner. 1.4 Problem Statement & Definitions A typical WLAN positioning setup is depicted Fig.1.1 where a user operates a wireless device equipped with WLAN communication capabilities. This device exchanges probe signals with L WLAN APs in the environment and measures RSS values through its network interface card. Since these APs may belong to different networks, their exact coordinates are generally unknown to the positioning system. Anchor point Access point Mobile user Fingerprint database Figure 1.1: The problem setup. Denote the true and estimated positions of the pedestrian with the mobile device at time

22 1.4. Problem Statement & Definitions 6 k as p(k) and ˆp(k). The objective of the WLAN tracking system is to determine a sequence of estimates of position over time, ˆp 1,..., ˆp(k), given a sequence of RSS measurements over time R(k) {r(0),..., r(k)}. In this thesis, the term memoryless positioning is used when ˆp(k) is computed based on information from the current observation r(k) only. The term dynamic positioning or tracking is used to refer to the estimation of ˆp(k) based on the entire observation record R(k) as well as past positioning estimates ˆp(0),..., ˆp(k 1) Positioning Architectures Depending on where RSS sensing and position estimation operations occur in the setup of Fig.1.1, the architectural design of positioning systems is categorized as centralized or decentralized. To promote user privacy, all designs in this thesis require that the position estimate is computed by the mobile device. probe signal probe signal probe signal position estimate position estimate position estimate probe signal Estimation probe signal position estimate Fusion position estimate probe signal probe signal position estimate position estimate (a) Centralized positioning (measurement fusion). (b) Decentralized positioning (estimation fusion). Figure 1.2: Overview of architectural design of WLAN positioning systems. 1. Centralized positioning (measurement fusion): As shown in Fig.1.2(a), in this

23 1.4. Problem Statement & Definitions 7 type of architecture, sensors (APs) act as observers that provide the mobile client with probe signals. The mobile client serves as the central node and uses RSS measurements obtained from probe signals to carry out positioning operations. Centralized fusion systems generally have the best performance in terms of accuracy as they have access to the complete observation set from all APs [28]. This set can be used for outlier filtering and faulty sensor detection [29]. The communication needs are minimal for this type of architecture: probe requests are sent by the mobile client to each AP and probe responses are received by the mobile from the APs. Such a centralized architecture also promotes privacy and security of position calculations. These advantages come at the cost of increased processing demand on the mobile client. Moreover, in centralized processing it is generally assumed that measurement from spatially distributed APs are conditionally independent. This, however, may not always be the situation depending on the geometry of the environment. With the exception of [30], this architecture is used in all WLAN positioning techniques, including this thesis. 2. Decentralized positioning (estimation fusion): This architecture is shown in Fig.1.2(b). Here each AP measures the RSS from the mobile client and estimate the position of the device based on this local RSS measurements. The local estimates are then sent to the mobile device where they are fused to form the global position estimate. An advantage of such a hierarchical scheme is the reduction of computational load on the power limited mobile. Moreover, each local estimate can be augmented with a quality measure for use in sensor selection based on reliability and fidelity of local estimates. Lastly, this architecture provides a scalable positioning solution in environments with a large number of APs. The disadvantage of this approach is the increased communication cost and the need for encryption of local estimates to ensure sufficient security. This architecture is used in [30] to promote resiliency to RSS-based

24 1.5. Key Technical Challenges 8 attacks on positioning systems. 1.5 Key Technical Challenges WLAN positioning systems rely on the dependency of RSS on the position of the mobile device. The key technical challenge in this area is estimation in presence of unpredictable variations in RSS. The variations occur due to radio channel impediments such as interference and shadowing by moving objects [31] as well as movement and orientation changes of the actual wireless device. Fig.1.3 shows an example of such RSS variations at a fixed location RSS (dbm) Sample Number Figure 1.3: Example of RSS measurements over time at a fixed location. The dynamic and unpredictable propagation characteristics [32] of indoor WLANs mean that in general, an explicit formulation of the RSS-position relation is impossible. Therefore, WLAN positioning systems characterize the RSS-position relationship implicity, through the a training-based method known as location fingerprinting or scene matching. In this approach, training RSS measurements are collected at a set of anchor points with known positions. It is this training-based characterization of the RSS-position dependency that differentiates the WLAN problem from that of GPS positioning.

25 1.5. Key Technical Challenges 9 To obtain a position estimate during the operation of the system, the incoming readings from the mobile are matched against these fingerprints. This matching process is challenging because the unpredictable time variation of the radio propagation conditions lead to a manyto-many RSS-position relationship. Moreover, environmental variations, such as presence of people and movement of furniture, may cause RSS characteristics observed during the on-line operation of the system to deviate from those learned based on location fingerprinting. The aforementioned conditions make RSS-based positioning difficult and often unreliable. This motivates the use of an additional source of information to augment RSS measurements. In particular, since pedestrian motion adheres to laws of kinematics, positions occupied by a mobile device over time are correlated. Therefore, knowledge of motion dynamics can be used as an additional source of information during positioning [33 35]. In particular, the use of such model allows for dynamic positioning or tracking whereby past location estimates, in addition to current RSS measurements, contribute to the calculation of the current position estimate. WLAN tracking is a challenging problem because in contrast to classical target tracking problems, an explicit form relating position to RSS measurements is unknown. Instead, training RSS values, collected at a set of spatially distributed anchor points, implicity characterize the RSS-position dependency through location fingerprinting. This nonparametric characterization renders the commonly used Kalman filter and its variants inapplicable to the WLAN tracking problem. The difficulty of this problem resulting from the absence of an explicit RSS-position relationship, has limited rigorous treatment of this subject. Consequently, new developments in the area of stochastic filtering are needed to handle the unique characteristics of the WLAN tracking problem.

26 1.6. Thesis Contributions Thesis Contributions This thesis proposes a framework for RSS-based WLAN positioning and tracking using nonparametric statistical techniques to address the challenges discussed in the previous section. The specific contributions of the thesis are as follows. 1. Memoryless estimation: The first contribution of this thesis is the use of nonparametric kernel density estimation for memoryless positioning. Two statistical estimators were derived to approximate the minimum mean square error position estimate using a nonparametric characterization of the RSS-position dependency. In addition to improving positioning accuracy as compared to the state-of-the-art, the kernel density formulation serves as the basis for development of a positioning solution that incorporates pedestrian motion dynamics as an additional source of information during estimation. This contribution appears in parts in [25, 30, 33]. 2. Dynamic positioning (tracking): The second contribution of this thesis addresses the combination of motion dynamics with RSS measurements for enhancing positioning accuracy. The first rigorous treatment of state-space filtering in the area of WLAN positioning is provided and the Nonparametric Information (NI) filter is introduced. This novel filter combines position estimates from the memoryless estimator with predictions based on a kinematics model of pedestrian motion. In contrast to the Kalman filter, the NI filter requires neither an explicit RSS-position relationship nor the knowledge of measurement noise statistics. Instead, the nonparametric memoryless estimator is used to quantify the contribution of RSS measurement using the location fingerprint information. This filter does not suffer from the high computational complexity of the particle filter and yet effectively tracks pedestrian motion under nonlinear and non Gaussian measurement conditions. In particular, experiments conducted on real data

27 1.7. Thesis Organization 11 from an office environment show that the NI filter leads to a 1.12m (20%) decrease in positioning error when compared to memoryless estimation. The formulation of the NI filter is a significant departure from existing literature, where the fingerprinting-based representation of RSS-position relationship had previously limited the development of rigorous mathematical tools for WLAN tracking. This contribution appears in [36]. 3. Cognitive positioning: The third contribution of this thesis is the design of a cognitive dynamic system to deal with uncertainties that result from unpredictable variations of RSS in indoor environments. In particular, the pedestrian motion model employed by the NI filter allows for the development a proactive system where anchor points and APs used in positioning are selected based on the anticipated position of the user. This system mitigates the adverse effects of time variations of RSS by restricting the filter operation to an adaptively determined region of interest over space. An strategy is presented for detection and mitigation of outlier RSS measurements to prevent the propagation of error through the feedback mechanism. Experimental results indicate that the cognitive design, and the proposed AP selection method provide an improvement of 3.19m (56%) in positioning error relative to memoryless positioning alone. This contribution appears in parts in [33, 36]. 1.7 Thesis Organization The rest of this thesis is organized as follows. Chapter 2 discusses relevant background material and provides an outline of existing methods addressing the WLAN tracking problem. A description of the major components of a WLAN positioning system is provided and relevant work in modeling of RSS characteristics, sensor selection, static and dynamic positioning (tracking) are reviewed.

28 1.7. Thesis Organization 12 The tools and designs proposed in this thesis are evaluated using RSS measurements from a real office environment. Chapter 3 provides a detailed description of this experimental setup, data sets, and testing scenarios used in this evaluation. This chapter also introduces the Average Root Mean Square Error (ARMSE) as the evaluation criterion throughout this thesis. The details of proposed memoryless estimation are presented in Chapter 4. A brief overview of kernel density estimation is also provided for completeness. Lastly, an extensive evaluation of the positioning accuracy of the proposed estimators as well as their sensitivity to parameter choices is provided. Chapter 5 introduces and evaluates the NI filter. In doing so, an overview of the Bayesian tracking problem is provided and the challenges unique to the WLAN tracking problem are discussed. The details of the pedestrian motion model assumed in this thesis are discussed and the derivation of the NI filter is presented. Finally, the positioning accuracy of the NI filter and its sensitivity to design parameters are experimentally evaluated. Chapter 6 motivates the cognitive design and presents the details of anchor point and AP selection methods proposed in this thesis. Experimental evaluation of the design as well as visual examples of tracking results are provided. Chapter 7 concludes this thesis with discussion of the significance of the contributions and results and suggests directions for future work.

29 Chapter 2 Background & Related Work This chapter identifies and discusses the various issues involved in designing a WLAN positioning system and reviews the state-of-the-art in each area. Fig.2.1 depicts the steps that comprise a WLAN positioning system. The first step is the determination of the dependency between the received signal strength (RSS) and position. This is a challenging task in indoors due to severe multipath and shadowing conditions and non-line-of-sight propagation caused by the presence of walls, humans, and other rigid objects. Therefore, the RSS-position dependency is characterized based on training measurement using either radio propagation modeling or fingerprinting-based techniques. Section 2.1 discusses these two approaches in further detail. During the on-line operation of the system, the mobile device measures an RSS observation and the positioning system uses RSS-positioning relationship learned during the training phase. Because the indoor propagation environment is highly non-stationary, RSS observations may deviate from those in the radio map, degrading the positioning accuracy. With reference to Fig.2.1, two preprocessing steps aim to mitigate the adverse effects of such deviations. The first step constrains the positioning algorithm to the relevant portions of the space. The second preprocessing step is AP selection: the choice of a subset of the available 13

30 2.1. RSS-Position Dependency 14 APs for positioning to reduce computational complexity and biases caused by geometrical configuration of APs. These two steps are discussed in Sections 2.2 and 2.3. The next step is to use the training information together with the RSS observation to estimate the position of the mobile device. Mathematical foundations and existing solutions in this area are review in 2.4. The last step in WLAN positioning is tracking. This entails augmenting RSS measurements with knowledge of motion dynamics to improve positioning accuracy. The motivation behind the problem of tracking and related solutions and tools are presented in detail in Section 2.5. POSITION-RSS DEPENDENCY RSS VECTOR PRE-PROCESSING AP SELECTION POSITIONING PEDESTRIAN TRACKING -Propagation model -Fingerprinting -Clustering -Anchor point selection (novel contribution) -Strongest signal -Information theory -Bhattacharyya distance (novel contribution) -Discrimination score (novel contribution) -Nearest Neighbour -Probabilistic -Pattern recognition -MMSE estimation (novel contribution) -Time average -Kalman filter -Particle filter -Markov models -NI filter (novel contribution) Figure 2.1: Overview of RSS-based WLAN positioning. 2.1 RSS-Position Dependency WLAN positioning systems rely on the dependency of RSS on location of the mobile device. Unfortunately, an explicit functional RSS-position relationship is not available in indoor environments because of the complexity of the indoor radio channel. This is due to severe multipath and shadowing conditions and non-line-of-sight propagation caused by presence of walls, humans, and other rigid objects. Moreover, the IEEE WLAN operates on the license-free band of frequency of 2.4GHz which is the same as cordless phones, microwaves, BlueTooth devices, and the resonance frequency of water. This means that WLAN systems are susceptible to time-varying interference from such devices and signal absorption by the human body, further complicating the propagation environment. This type of environment

31 2.1. RSS-Position Dependency 15 gives rise to a many-to-many correspondence between RSS and spatial positions. To make matters worse, WLAN infrastructures are highly dynamic as access points can easily be moved or discarded, in contrast to their base-station counterparts in cellular systems which generally remain intact for long periods of time. Existing WLAN positioning techniques use two approaches to modeling the RSS-position dependency: radio propagation modeling [37, 38] and fingerprinting [21, 24, 25, 39 43] Radio Propagation Modeling This approach entails the assumption of a prior theoretical model for the RSS-position relationship and estimation of model parameters based on training data. Given an RSS measurement and this model, the distances from the mobile device to at least three APs are determined and trilateration is used to obtain the position of the device. As the radio signal travels through an ideal propagation medium (i.e., free-space), the received signal power falls off inversely proportional to the square of the distance. Thus, given measurements of transmitted and received powers, the distance between the transmitter and the mobile device can be determined. In real environments, however, in addition to the distance traveled two additional mechanisms contribute to variations in the propagation channel namely, large scale and small scale fading [44]. Large scale fading is due to path loss and shadowing effects. Path loss is in turn related to dissipation of signal power over distances of meters. Shadowing is a result of reflection, absorption, and scattering caused by obstacles between transmitter and receiver and occurs over distances proportional to the size of the objects [31]. Due to uncertainties in the nature and location of the blocking objects, the effects of shadowing are often characterized statistically. Specifically, a lognormal distribution is generally assumed for the ratio of transmit-to-receive power. The combined effects of the path loss and shadowing can be expressed by the simplified model

32 2.1. RSS-Position Dependency 16 shown below [31]. P r (db) = P t (db) + 10 log 10 K 10γ log 10 ( d d 0 ) ψ(db). (2.1) In Equation (2.1), K is constant relating to antenna and channel characteristics, d 0 is a reference distance for antenna far-field, and γ is the path loss exponent. Typical values of this parameter are γ = 2 for free-space and 2 γ 6 for an office building with multiple floors. Finally, ψ N (0, σψ 2 ) reflects the effects of log-normal shadowing in the model. In indoor areas, materials for walls and floors, number of floors, layout of rooms, location of obstructing objects, and size of each room have a significant effect on path loss. This makes it difficult to find a model applicable to general environments. Small scale fading is due to constructive and destructive addition from multiple signal paths (multipath) and happens over distances on the order of the carrier wavelength (for WLANs, λ = c 2.4GHz = 12.5cm) [31]. These effects are not explicitly considered in WLAN positioning due to modeling complexity resulting from the time-varying environment. Note that the model of (2.1) assumes of isotropic RSS contours. This is not the case in indoor propagation environment were the distribution of blocking objects, such as walls and furniture, is asymmetric. Moreover, this model is invariant to receiver orientation [45]. Yet, several experimental works have shown that RSS is in fact dependent on the orientation of device [45]. Lastly, model-based approaches assume exact knowledge of AP locations. This is impractical in large and ubiquitous deployments where multiple wireless networks, run by different operators, coexist Fingerprinting-Based Methods To overcome the above limitations of the propagation model, the second class of WLAN positioning systems use a training-based method known as location fingerprinting [21, 24,

33 2.1. RSS-Position Dependency 17 25, 39 43]. The objective of these methods is to implicity characterize the spatio-temporal properties of RSS through training RSS measurements at spatially distributed anchor points with known coordinates. The training data is used to construct a radio map defined as follows R {(p i, F(p i )) i = 1... N}, (2.2) where p i [p x p y ] T is the Cartesian coordinates of the i th anchor point, F(p i ) [r i (1)... r i (n)] is a fingerprint matrix [25], and n is the number of training samples collected at each anchor point. The vector r i (τ) [ri 1 (τ),..., ri L (τ)] contains the RSS measurements from L APs at time τ at spatial point p i. Note that n RSS samples are collected at each anchor point to obtain information regarding the temporal variations of RSS at fixed positions caused by time-varying multipath and shadowing. The number of anchor points and their placement as well as the number of time samples collected are design decisions affecting the complexity and accuracy of the radio map. Due to the complexity of the RSS-position relationship, the effect of these parameters have been studied only experimentally with the exception of [45] where theoretical guidelines are provided by assuming Gaussian RSS distributions. The usefulness of these guidelines, however, is not clear as the Gaussian assumption is violated in indoor WLAN settings. Traditionally, radio map construction has been performed prior to the operation of the positioning system during an off-line training session. Since the RSS propagation environment is time-varying, however, the usefulness of the learned radio decreases overtime and regular updates become necessary. More recently, on-line determination of the training values has been proposed in [39, 46] to improve the reliability of fingerprints and promote resiliency to time variations in indoor radio environments. These systems use a set of reference RSS receiver to sense the environment continuously and adapt the entire radio map accordingly

34 2.2. Region of Interest Determination 18 through interpolation and regression analysis. Another limitation of fingerprinting approaches lies in the dependence of RSS on the choice of network card used for measurements. In particular, the RF energy reading at the antenna of the mobile client is continuous in nature and is converted to an integer value by the network card. Since RSS readings are intended to be used internally and in a relative manner for network functions, the IEEE standard specifies neither how this mapping is done nor the accuracy of reported RSS values. Consequently, this mapping is performed based on vendor-specific conventions, leading to variations in reported RSS values from different cards. For example, the experimental observations of [47] indicate variations up to 27dBm between network cards made by Intel and D-Link. To mitigate the discrepancies arising from the use of different network cards, the work of [48] proposes the use of signal strength ratios between pairs of APs as opposed to RSS for fingerprinting. Despite the above limitations, location fingerprinting is shown to provide an effective characterization of the RSS-position dependency for WLAN positioning. This is the method of choice in most existing literature [21, 24, 25, 39 43] and this thesis. 2.2 Region of Interest Determination Since the indoor propagation channel varies over time, RSS observations received during the on-line operation of the system may deviate from those stored in the radio map. To mitigate the effects of such deviations, a pre-processing step is performed to constrain positioning algorithm to the relevant portions of the space. The work of [49] proposes two methods for clustering of spatial locations based on covering APs to decrease computational complexity and improve accuracy and scalability of the positioning algorithm. The first method [50] involves an off-line clustering of locations aiming to reduce the search space to a single cluster. The second proposes an incremental trilateration technique based on the available APs at

35 2.3. Access Point Selection 19 the observation point. The work of [51] considers the similarity of signal values as well as the set of covering APs to generate a set of clusters using K-means to improve power efficiency of mobile devices. The incoming observation is then matched to one of the clusters and positioning is restricted this cluster only. Both of the above clustering techniques are carried out off-line based on the training data. This hampers the operation of the system over time since WLAN infrastructures are highly dynamic and APs can easily be moved or discarded, in contrast to their base-station counterparts in cellular systems which generally remain intact for long periods of time. More importantly, these methods assume that locations that are close in the physical space receive similar AP coverage and RSS readings. In practice, this assumption is often violated in asymmetric indoor WLAN propagation environments where a many-to-many RSS-position relationship exists [30]. As an alternative to the above RSS-based schemes, this thesis proposes the use of a feedback system where a region of interest (ROI) is determined based on the predicted positions of the user. The experimental results indicate that this type of anticipatory design is effective in significantly increasing positioning accuracy. 2.3 Access Point Selection The third fingerprinting challenge is that of AP selection. Estimation of a position in a two dimensional space requires measurements from at least three APs. Due to the wide deployment of APs, the dimension of the measurement vector (L) is generally much greater than the minimum 3 needed for positioning. Recall that at a spatial point p, the RSS from an AP a is a function of the distance between p and a as well as the environmental factors in terms of obstacles causing NLOS propagation. Therefore, depending on the topology of the environment subsets of available APs may report correlated readings, leading to needless redundancy and possibly biased estimates. This motivates the need for an AP selection

36 2.4. Memoryless Positioning 20 component to choose a subset of the available APs for use in positioning. Surprisingly, this problem has received little attention in the existing literature [51]. Selection methodologies have been limited to choosing a subset of APs with the highest observation RSS arguing that the strongest APs provide the highest probability of coverage over time [50]. The strongest APs, however, may not always provide the best positioning accuracy as shown in [51]. It is known that variance of measurements from an AP increases with its mean power at a given location. In cases where the measured RSS from an AP exhibits a high degree of variance, the anchor values may be very different than the on-line measurement, degrading the accuracy of estimation [43]. Furthermore, it becomes more difficult to distinguish neighboring points in such cases. The recent work of [51] offers a selection strategy based on the discriminant power of each AP quantified through the entropy-based Information Gain criterion. This thesis examines two other types of selection criteria derived based on the principles of minimizing redundancy and maximizing discrimination ability. An important factor to consider in AP selection is that RSS statistics are spatially dependent [25]. Therefore, any selection criterion depending on these statistics should be applied locally over small regions of space. For this reason, the thesis proposes the use of the ROI for evaluation of selection criteria. 2.4 Memoryless Positioning Recall that the radio map constructed during fingerprinting consisted of pairs {(p i, F(p i )) i = 1... N}. The two preprocessing steps of ROI determination and AP selection result in a modified radio map R(k) = {(p i, F(p i, k))} N(k) i=1 at each time step. Given the RSS observation r(k), this map is used to compute a position estimate ˆp(k) = g(r(k), R(k)). The function g(, ) provides a mapping between the observation RSS and anchor point location. Various approaches to determination of this mapping are available in the literature. In