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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
The essential guide to state-of-the art mobile positioning and tracking techniques—fully updated for new and emerging trends in the field
Mobile Positioning and Tracking, Second Edition explores state-of-the-art mobile positioning solutions applied on top of current wireless communication networks. Application areas covered include positioning, data fusion and filtering, tracking, error mitigation, both conventional and cooperative positioning technologies and systems, and more. The authors fill the gap between positioning and communication systems, showing how features of wireless communications systems can be used for positioning purposes and how the retrieved location information can be used to enhance the performance of wireless networks.
Unlike other books on the subject, Mobile Positioning and Tracking: From Conventional to Cooperative Techniques, 2nd Edition covers the entire positioning and tracking value chain, starting from the measurement of positioning signals, and offering valuable insights into the theoretical fundamentals behind these methods and how they relate to application areas such as location-based services, as well as related disciplines and professional concerns, including global business considerations and the changing laws and standards governing wireless communication networks.
Fully updated and revised for the latest developments in the field, this Second Edition:
- Features new chapters on UWB positioning and tracking, indoor positioning in WLAN, and multi-tag positioning in RFID
- Explores an array of positioning and tracking systems based on satellite and terrestrial systems technologies and methods
- Introduces advanced and novel topics such as localisation in heterogeneous and cooperative scenarios
- Provides a bridge between research and industry with potential implementations of the solutions presented
Mobile positioning and tracking is subject to continuous innovations and improvements. This important working resource helps busy industry professionals and practitioners—including software and service developers—stay on top of emerging trends in the field. It is also a valuable reference for advanced students in related disciplines studying positioning and mobile technologies.
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Veröffentlichungsjahr: 2017
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Table of Contents
Cover
Title Page
Copyright
Dedication
About the Authors
List of Contributors
Preface
Acknowledgements
List of Abbreviations
Notations
Chapter 1: Introduction
1.1 Application Areas of Positioning (Chapter 2)
1.2 Basics of Wireless Communications for Positioning (Chapter 3)
1.3 Fundamentals of Positioning (Chapter 4)
1.4 Data Fusion and Filtering Techniques (Chapter 5)
1.5 Fundamentals of Tracking (Chapter 6)
1.6 Error Mitigation Techniques (Chapter 7)
1.7 Positioning Systems and Technologies (Chapter 8)
1.8 Ultrawideband Positioning and Tracking (Chapter 9)
1.9 Indoor Positioning in WLAN (Chapter 10)
1.10 Cooperative Multi-tag Localization in RFID Systems (Chapter 11)
1.11 Cooperative Mobile Positioning (Chapter 12)
Chapter 2: Application Areas of Positioning
2.1 Introduction
2.2 Localization Framework
2.3 Location-based Services
2.4 Location-based Network Optimization
2.5 Patent Trends
2.6 Conclusions
Chapter 3: Basics of Wireless Communications for Positioning
3.1 Introduction
3.2 Radio Propagation
3.3 Multiple-antenna Techniques
3.4 Duplexing Methods
3.5 Modulation and Multiple-access Techniques
3.6 Radio Resource Management and Mobile Positioning
3.7 Synchronization
3.8 Cooperative Communications
3.9 Cognitive Radio and Mobile Positioning
3.10 Conclusions
Chapter 4: Fundamentals of Positioning
4.1 Introduction
4.2 Classification of Positioning Infrastructures
4.3 Types of Measurements and Methods for their Estimation
4.4 Positioning Techniques
4.5 Error Sources in Positioning
4.6 Metrics of Location Accuracy
4.7 Conclusions
Chapter 5: Data Fusion and Filtering Techniques
5.1 Introduction
5.2 Least-squares Methods
5.3 Bayesian Filtering
5.4 Estimating Model Parameters and Biases in Observations
5.5 Alternative Approaches
5.6 Conclusions
Chapter 6: Fundamentals of Tracking
6.1 Introduction
6.2 Impact of User Mobility on Positioning
6.3 Mobility Models
6.4 Tracking Moving Devices
6.5 Conclusions
Chapter 7: Error Mitigation Techniques
7.1 Introduction
7.2 System Model
7.3 NLOS Scenarios: Fundamental Limits and Maximum-likelihood Solutions
7.4 Least-squares Techniques for NLOS Localization
7.5 Constraint-based Techniques for NLOS Localization
7.6 Robust Estimators for NLOS Localization
7.7 Identify and Discard Techniques for NLOS Localization
7.8 Conclusions
Chapter 8: Positioning Systems and Technologies
8.1 Introduction
8.2 Satellite Positioning
8.3 Cellular Positioning
8.4 Wireless Local/Personal Area Network Positioning
8.5 Ad hoc Positioning
8.6 Hybrid Positioning
8.7 Conclusions
Acknowledgements
Chapter 9: Ultra-wideband Positioning and Tracking
9.1 Introduction
9.2 UWB Technology
9.3 The UWB Radio Channel
9.4 UWB Standards
9.5 Time-of-arrival Measurements
9.6 Ranging Algoritms in Real Conditions
9.7 Passive UWB Localization
9.8 Conclusions and Perspectives
Acknowledgments
Chapter 10: Indoor Positioning in WLAN
10.1 Introduction
10.2 Potential and Limitations of WLAN
10.3 Empirical Approaches
10.4 Error Sources in RSS Measurements
10.5 Experimental Activities
10.6 Conclusions
Chapter 11: Cooperative Multi-tag Localization in RFID Systems: Exploiting Multiplicity, Diversity and Polarization of Tags
11.1 Introduction
11.2 RFID Positioning Systems
11.3 Cooperative Multi-tag Localization
11.4 Conclusions
Chapter 12: Cooperative Mobile Positioning
12.1 Introduction
12.2 Cooperative Localization
12.3 Cooperative Data Fusion and Filtering Techniques
12.4 COMET: A Cooperative Mobile Positioning System
12.5 Experimental Activity in a Cooperative WLAN Scenario
12.6 Conclusions
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Begin Reading
List of Illustrations
Chapter 1: Introduction
Figure 1.1 Schematic representation of a positioning system (a) and a positioning solution (b). The solid arrows represent the flow of communication and the dashed arrows represent the flow of position information. In (a), it is possible to see that position is always generated in a positioning technology. In contrast, a positioning solution is often calculated based on opportunistic information obtained from communication technology.
Chapter 2: Application Areas of Positioning
Figure 2.1 The Location Stack (Hightower et al. 2002).
Figure 2.2 An LBS as an intersection of technologies.
Figure 2.3 The LBS ecosystem.
Figure 2.4 Service model.
Figure 2.5 LBS application categories.
Figure 2.6 The social network matrix (partial list) (Ziv and Mulloth 2006).
Figure 2.7 Context management and distribution of context information to enable context-aware applications.
Figure 2.8 Positioning system based on a centralized estimation of the client's position.
Figure 2.9 Trade-off between accuracy and reliability of the information as a function of velocity. (a) Localization accuracy vs. velocity; (b) Information reliability vs. velocity.
Figure 2.10 Patenting trend within the last 20 years.
Figure 2.11 Patenting trend in terms of geography.
Figure 2.12 Patenting trend in terms of assignees.
Figure 2.13 Patenting trend within location-based gaming.
Figure 2.14 Top patent assignees within location-based gaming.
Figure 2.15 Patenting trend within the location-based community.
Figure 2.16 Top patent assignees within the location-based community.
Chapter 3: Basics of Wireless Communications for Positioning
Figure 3.1 Propagation loss (Prasad et al. 2009).
Figure 3.2 A spatial multiplexing system.
Figure 3.3 Single-carrier vs. multi-carrier modulation.
Figure 3.4 OFDM transceiver chain (Prasad et al. 2009).
Figure 3.5 An example of a cooperative communication scenario.
Figure 3.6 Cognitive radio and mobile positioning. PU, primary user; SU, secondary user.
Chapter 4: Fundamentals of Positioning
Figure 4.1 Classification according to system topology: (a) self-positioning; (b) remote positioning; (c) indirect self-positioning; (d) indirect remote positioning. Dashed lines represent a communication signal used for measuring the wireless channel, and solid lines represent an actual transfer of measured data.
Figure 4.2 Effect of shadowing on AOA measurements. The dotted line represents the direct angle between transmitter and receiver, and the dashed line represents the propagation path influenced by the shadowing effect.
Figure 4.3 Cell ID technique.
Figure 4.4 Triangulation technique.
Figure 4.5 Hyperbolic localization technique.
Figure 4.6 Angulation technique.
Figure 4.7 Database correlation technique: (a) calibration procedure; (b) localization procedure.
Figure 4.8 Video analysis technique.
Chapter 5: Data Fusion and Filtering Techniques
Figure 5.1 Local-minimum problem (the function has been chosen for illustrative purposes only).
Figure 5.2 Example of an application where a wireless target is tracked using three access points capable of measuring the distance to the target. The left plot shows the tracking result when fresh measurements are available every time the target moves 1 m. The right plot shows the equivalent result when fresh measurements are available every time the target moves 5 m.
Figure 5.3 Evolution of the Euclidean distance between the true position and the estimated position at every time step. The left plot was obtained for a setup where fresh measurements are available every time the target moves 1 m. The right plot was obtained for a setup where measurements are available every time the target moves 5 m.
Figure 5.4 Evolution of the trace of the covariance matrix at every time step. The left plot was obtained in a setup where fresh measurements are available every time the target moves 1 m. The right plot was obtained for a setup where measurements are available every time the target moves 5 m.
Figure 5.5 Each of these three plots shows the average and the confidence interval as a function of position. The top curve shows the top limit of the confidence interval, the middle curve shows the mean value and the bottom curve shows the bottom limit of the confidence interval. Each plot corresponds to a different AP.
Figure 5.6 These plots show, for each AP, the distribution of probability given by Equation (5.130), that is, the probability of placement of the MS given the measurements of power and the entire calibration data.
Figure 5.7 Probability density function of placement of the MS with respect to the measurements obtained.
Figure 5.8 Heat map representing the likelihood of the MS position. The darker the map, the more likely is the placement of the MS.
Chapter 6: Fundamentals of Tracking
Figure 6.1 Example of a Brownian motion.
Figure 6.2 Random walk in one dimension (left) and two dimensions (right). Note that the circles that identify the steps in the left plot are not used in the right plot for easier reading of the figure.
Figure 6.3 Example of a random waypoint walk movement.
Figure 6.4 Example of a Gauss–Markov movement with (left) and (right).
Figure 6.5 Example of a Markov chain motion model. The model assumes that the movements in each coordinate are independent from each other and that the MS can be in one of three states: static, decreasing by one unit or increasing by one unit.
Figure 6.6 Example path sampled from the Markov model shown in Figure 6.5.
Figure 6.7 Example of a Markov chain motion model. The velocity is assumed constant and the direction to be variable. Each change in direction in state 2 follows a Gaussian distribution.
Figure 6.8 Example path sampled from the Markov model shown in Figure 6.7.
Figure 6.9 Example of a map used by the Manhattan model.
Figure 6.10 Single run of the Manhattan model. At every crossing, the probability of continuing straight is 0.5, of turning either left or right is 0.25, and of turning back is 0.
Figure 6.11 Sample pattern obtained by a simulation of a reference point group mobility model. Each line represents the trajectory of a different individual.
Figure 6.12 Example of the correlation group mobility model with two different values for the correlation factor. In the left plot the correlation factor is 0, while in the right plot the correlation factor is 0.999.
Figure 6.13 Simulation of the sociability factor model. The left plot shows the initial random placement of the nodes and the right plot shows a simulation of a movement where nodes move two steps.
Figure 6.14 Example of the execution of the multiple-model framework. Two filters run in parallel for two units of time. The result is four histories.
Figure 6.15 Clustering positioning data in order to identify different areas. The right plot shows the data points in a hypothetical room. The left plot shows the execution of the EM algorithm and the classification of each positioning estimator.
Figure 6.16 Example of 15 noisy routes (left) and the corresponding linear regression (right).
Figure 6.17 True routes and routes estimated using the -means algorithm.
Chapter 7: Error Mitigation Techniques
Figure 7.1 Illustration of the NLOS problem in wireless localization.
Figure 7.2 Illustration of a simple scenario for wireless localization.
Figure 7.3 GDOPs for different topologies and MS locations (Guvenc and Chong 2009).
Figure 7.4 Simulations of exact and approximate ML techniques for NLOS mitigation.
Figure 7.5 Block diagrams for (a) ML estimator and (b) MAP estimator for NLOS scenarios. In (a), without loss of generality, it is assumed that the first measurements are the LOS measurements (Guvenc and Chong 2009).
Figure 7.6 Simulations for several different NLOS mitigation techniques.
Figure 7.7 Illustration of the CLS technique. The NLOS FRPs are used to determine the feasible region. For the original (nonlinear) model, the feasible region is obtained from the intersections of the circles. For the linear model, the feasible region is obtained from the intersections of the squares (Guvenc and Chong 2009).
Chapter 8: Positioning Systems and Technologies
Figure 8.1 Distance measurements to satellites define orbits intersecting in a point.
Figure 8.2 Virtual center in a symmetric cellular network.
Figure 8.3 Construction of the cell geometry.
Figure 8.4 Cell geometry of a GSM network in Duisburg, Germany (© 2009 Google – Map data © 2009 Tele Atlas).
Figure 8.5 Localization accuracy measured in the area of Duisburg, using the cell ID positioning method.
Figure 8.6 Channel attenuation obtained from RSSI measurements in the area of Duisburg.
Figure 8.7 Measured timing advance and measured distance in the city of Duisburg.
Figure 8.8 Localization accuracy measured in the area of Duisburg, using mobile-assisted TOA positioning.
Figure 8.9 Measurement route in Duisburg (© 2009 Google – Map data © 2009 Tele Atlas).
Figure 8.10 Median area of uncertainty (urban area).
Figure 8.11 Median area of uncertainty (rural area).
Figure 8.12 OTDOA positioning. UE, user equipment.
Figure 8.13 Deriving the hyperbolic function for the time delay.
Figure 8.14 Positioning techniques in LTE.
Figure 8.15 OTDOA techniques in LTE.
Figure 8.16 Emergency caller localization system in a mobile network.
Figure 8.17 RMS localization error in UWB scenarios.
Figure 8.18 RSSI thresholds in Bluetooth.
Figure 8.19 RF level vs. RSSI.
Figure 8.20 Road-mounted RFID tags.
Figure 8.21 Infrared positioning.
Figure 8.22 Ultrasonic positioning and orientation estimation.
Figure 8.23 Conceptual architecture of combined cellular and WLAN positioning.
Figure 8.24 A-GPS conceptual architecture.
Chapter 9: Ultra-wideband Positioning and Tracking
Figure 9.1 Example of a UWB modulated signal.
Figure 9.2 Example of measured UWB multipath profile in LOS condition.
Figure 9.3 Specular and diffuse multipath components.
Figure 9.4 IEEE 802.15.4a transmission chain.
Figure 9.5 IEEE 802.15.4a signal structure.
Figure 9.6 The IEEE 802.15.4a packet structure.
Figure 9.7 The ISO/IEC FDIS 24730 12 bytes minimal blink packet.
Figure 9.8 Two-way ranging.
Figure 9.9 ML TOA estimator in AWGN.
Figure 9.10 Possible LOS and NLOS configurations. RX1, LOS condition; RX2, NLOS condition, no DP blockage; RX3, NLOS condition, DP blockage.
Figure 9.11 TOA estimator with ED scheme.
Figure 9.12 Illustration of the Max, -Max, simple thresholding, JBSF, SBS and SBSMC algorithms (from Dardari et al. 2009).
Figure 9.13 RMSE as a function of SNR for different ED-based TOA estimation schemes using the IEEE 802.15.4a channel model (from Dardari et al. 2009).
Figure 9.14 SDS ranging protocol.
Figure 9.15 DR-TWR ranging protocol.
Figure 9.16 True trajectory of the mobile node (solid line), estimated positions of the mobile node (dots) and position of the anchors using M3.
Figure 9.17 Estimated CDF of the position error for the four mobility models considered. in model M3.
Figure 9.18 UWB-RFID backscattering scheme.
Figure 9.19 Example of measured backscattered signal for tag open and short circuit loads (only the antenna mode component is shown).
Figure 9.20 Luggage sorting on a conveyor belt using UWB-RFID tags (Guidi et al. 2016).
Figure 9.21 UWB-RFID tag with energy harvesting in the UHF band.
Figure 9.22 Single-port UHF/UWB antenna prototype on paper substrate (Fantuzzi et al. 2015).
Figure 9.23 Energy transfer mechanism to localize and energize passive tags using mmW/THz massive antenna arrays.
Chapter 10: Indoor Positioning in WLAN
Figure 10.1 Empirical scenario (Della Rosa et al. 2012).
Figure 10.2 Broadcasted frames (Della Rosa et al. 2012).
Figure 10.3 Cell-ID (Della Rosa et al. 2012).
Figure 10.4 Fingerprinting phases (Della Rosa et al. 2012).
Figure 10.5 Fingerprinting map (Della Rosa et al. 2012).
Figure 10.6 Path loss (Della Rosa et al. 2012).
Figure 10.7 Empirical path-loss phases (Della Rosa et al. 2012).
Figure 10.8 Empirical path loss (Della Rosa et al. 2012).
Figure 10.9 Long-range and short-range RSS fluctuations (Della Rosa et al. 2012).
Figure 10.10 Error sources in RSS (Della Rosa et al. 2012).
Figure 10.11 RSS comparison for heterogeneous WiFi cards (Della Rosa et al. 2012).
Figure 10.12 Different handheld device orientations (Della Rosa et al. 2012).
Figure 10.13 RSS values for different handheld WiFi orientation (Della Rosa et al. 2012).
Figure 10.14 Hand grip (Della Rosa et al. 2012).
Figure 10.15 Hand-grip effect on RSS measurements and distance estimations (Della Rosa et al. 2012).
Figure 10.16 Hand-grip effect on position estimation without (left) and with (right) mitigation (Della Rosa et al. 2012).
Figure 10.17 Body-loss effect on RSS measurements and distance estimations (Della Rosa et al. 2012).
Figure 10.18 Body-loss effect on position estimation without (left) and with (right) mitigation (Della Rosa et al. 2012).
Chapter 11: Cooperative Multi-tag Localization in RFID Systems: Exploiting Multiplicity, Diversity and Polarization of Tags
Figure 11.1 Typical RFID system.
Figure 11.2 RFID tag.
Figure 11.3 (a) LOS/NLOS and (b) polarization match/mismatch.
Figure 11.4 Localization of a package in a warehouse.
Figure 11.5 Localization of a person in a room.
Figure 11.6 Localization of a hand-held RFID reader by means of a high density of reference tags.
Figure 11.7 Localization of a mobile RFID reader by means of a low density of reference tags.
Figure 11.8 (a) AOA by multiple antenna reader. (b) CoopAOA by reference tag-pair.
Figure 11.9 Unmodulated RF carrier at 900 MHz.
Figure 11.10 Pilot bit sequence (top) and ASK-modulated signal (bottom).
Figure 11.11 BPSK-modulated signal.
Figure 11.12 BPSK-modulated signal received at the reader from Tag
1
(top) and Tag
2
(bottom).
Figure 11.13 AOA determination from TDOA.
Figure 11.14 Simulation scenario (perspective view).
Figure 11.15 Tag configurations with: (a) a single tag; and (b) multiple tags.
Figure 11.16 Simulation scenario (top view).
Figure 11.17 Comparison between single and multiple active tags.
Figure 11.18 Comparison between single and multiple passive tags.
Figure 11.19 Effect of polarization on localization accuracy.
Figure 11.20 CDFs of the localization error in height and corresponding accuracy for a lying target.
Figure 11.21 CDFs of the localization error in height and corresponding accuracy for a sitting target.
Figure 11.22 CDFs of the localization error in height and corresponding accuracy for a standing target.
Figure 11.23 CoopAOA vs RSS and TDOA-RSS.
Figure 11.24 Localization accuracy of CoopAOA with two and four tag-pairs.
Figure 11.25 Coverage area for the simulation scenario with passive tag-pairs.
Figure 11.26 Error performance of CoopAOA with active and passive tags.
Figure 11.27 Outage probability for CoopAOA with passive and active tags.
Figure 11.28 Energy–cost–accuracy comparison of different configurations.
Figure 11.29 Scenario of the experimental setup.
Figure 11.30 A reader mounted on a chair.
Figure 11.31 Arrangement of the four tags.
Figure 11.32 Different curve fittings for reader 1.
Figure 11.34 Different curve fittings for reader 3.
Figure 11.35 Localization accuracy with a different number of tags.
Chapter 12: Cooperative Mobile Positioning
Figure 12.1 Cooperative localization in a network of mobile robots.
Figure 12.2 Taxonomy of localization techniques for WSNs.
Figure 12.3 Cooperative localization in WSNs.
Figure 12.4 Clustering in WSNs.
Figure 12.5 Typical “urban canyon” scenario.
Figure 12.6 COMET: system architecture.
Figure 12.7 Operational representation of one-level data fusion (1L-DF).
Figure 12.8 Schematic representation of the general scenario considered by the 2L-DF algorithm.
Figure 12.9 Operational representation of 2L-DF.
Figure 12.10 LOS probability vs separation distance between TX and RX (Frattasi and Monti 2007a).
Figure 12.11 The seven-cell and one-cluster system layout considered (Frattasi et al. 2006).
Figure 12.12 (C)RMSE vs number of CMs (HTAP case). (S. Frattasi, M. Monti, “Cooperative Mobile positioning in 4G wireless networks”,
Cognitive Wireless Networks: Concepts, Methodologies and Visions, Inspiring the Age of Enlightenment of Wireless Communications
, Springer, pp. 213–233, September, 2007. Reproduced in part by kind permission of © Springer Science and Business Media.)
Figure 12.13 (C)RMSE vs. number of CMs (HLOP case). (S. Frattasi, M. Monti, “Cooperative Mobile positioning in 4G wireless networks”,
Cognitive Wireless Networks: Concepts, Methodologies and Visions, Inspiring the Age of Enlightenment of Wireless Communications
, Springer, pp. 213–233, September, 2007. Reproduced in part by kind permission of © Springer Science and Business Media.)
Figure 12.14 (C)RMSE vs. number of CMs (HTDOA case).
Figure 12.15 (C)RMSE vs number of BSs (HLOP case). (S. Frattasi, M. Monti, “Ad-Coop positioning system (ACPS): Positioning for cooperative users in hybrid cellular ad-hoc networks”,
European Transactions on Telecommunications Journal
, Wiley, vol. 19, no. 8, pp. 923–924, May, 2007. Reproduced in part by permission of © 2007 John Wiley & Sons Ltd.)
Figure 12.16 (C)RMSE vs number of BSs (HTDOA case).
Figure 12.17 Average (C)RMSE vs number of CMs and clustering method (HTAP case).
Figure 12.18 Cooperative scenario (Della Rosa et al. 2012).
Figure 12.19 Cooperative scenario (Della Rosa et al. 2013).
Figure 12.20 Noncooperative case estimated positions (Della Rosa et al. 2013).
Figure 12.21 Cooperative case estimated positions obtained by exploiting nearby RSS measurements from neighboring devices (Della Rosa et al. 2013).
List of Tables
Chapter 2: Application Areas of Positioning
Table 2.1 Examples of emergency, safety and security services. (Reproduced with kind permission of © 2007 Springer Science and Business Media).
Table 2.2 FCC requirements.
Table 2.3 QoS for pedestrians (Dhar and Varshney 2011; Machaj et al. 2012; 3GPP 2015)
Table 2.4 QoS for vehicles (Dhar and Varshney 2011; Machaj et al. 2012; 3GPP 2015)
Chapter 4: Fundamentals of Positioning
Table 4.1 Classification of positioning solutions based on system topology
Table 4.2 Classification of positioning solutions based on physical coverage range
Table 4.3 Classification of positioning solutions based on the type of integration
Chapter 7: Error Mitigation Techniques
Table 7.1 Overview of TOA-based localization algorithms for use in LOS and NLOS scenarios
Chapter 8: Positioning Systems and Technologies
Table 8.1 Mapping between the RSSI parameter in GSM and the actual value of received signal strength in dBm
Table 8.2 5G Positioning Accuracy and Use-Cases
Chapter 9: Ultra-wideband Positioning and Tracking
Table 9.1 LDC parameters
Table 9.2 Worldwide UWB emission masks
Table 9.3 Ranging error with clock drift
Chapter 11: Cooperative Multi-tag Localization in RFID Systems: Exploiting Multiplicity, Diversity and Polarization of Tags
Table 11.1 Simulation parameters
Table 11.2 Height accuracy (%) for single- and multi-tag scenarios
Table 11.3 Costs of readers and tags
Table 11.4 Reader (Wavetrend L-RX201)
Table 11.5 Active tag (Wavetrend)
Chapter 12: Cooperative Mobile Positioning
Table 12.1 Parameter settings for different environmental types (Greenstein et al. 1997). (S. Frattasi, M. Monti, “Cooperative mobile positioning in 4G wireless networks”,
Cognitive Wireless Networks: Concepts, Methodologies and Visions Inspiring the Age of Enlightenment of Wireless Communications
, Springer, pp. 213–233, September, 2007. Reproduced in part by kind permission of © Springer Science and Business Media.)
Table 12.2 Simulation parameters (S. Frattasi, M. Monti, “Cooperative mobile positioning in 4G wireless networks”,
Cognitive Wireless Networks: Concepts, Methodologies and Visions Inspiring the Age of Enlightenment of Wireless Communications
, Springer, pp. 213–233, September, 2007. Reproduced in part by kind permission of © Springer Science and Business Media.)
Table 12.3 (C)RMSE statistics for a variable number of CMs and BSs
Mobile Positioning and Tracking
From Conventional to Cooperative Techniques
Second Edition
Simone Frattasi
Sony Mobile Communications, Sweden
Francescantonio Della Rosa
Radiomaze Inc., USA, Tampere University of Technology, Finland
This edition first published 2017
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… to my dear dad, Luigi, and my grandad, Francesco
Simone Frattasi
… to my lovely wife Anna, my mom Emilia, my dad Liberto and my brother Gianluca
Francescantonio Della Rosa
About the Authors
Simone Frattasi became European Patent Attorney (EPA) in 2016. He received his PhD in wireless and mobile communications from Aalborg University (AAU), Aalborg, Denmark, in 2007, and his MSc degree cum laude and his BSc degree in telecommunications engineering from the University of Rome “Tor Vergata”, Italy, in 2002 and 2001, respectively. Additionally, he obtained a certificate as a project manager from Act2Learn in 2009 and a certificate as an instructor in the IEEE Leadership Course from the IEEE in 2008.
He is the head of the Patent Section at Sony Mobile Communications, Lund, Sweden, where he was previously employed as a senior patent attorney. From 2011 to 2015, he worked as a patent consultant at Patrade. From 2010 to 2011, he worked as a postdoc in the Center for TeleInFrastruktur (CTIF) at AAU, where he was technical project manager for the FP7 project ASPIRE, proposal coordinator for the SOS-4-HEALTH project (including AAU, Aalborg Hospital, Telenor, Care4All, IctalCare, and G4S) and lecturer for the course “IPR, Patenting and Technology Transfer” for the M.Sc. on innovative communication techniques and entrepreneurship. In 2009, he worked as a patent consultant in Plougmann & Vingtoft. From 2007 to 2008, he worked as a postdoc at AAU, where he was technical project manager for the industrial project LA-TDD, a collaboration with Nokia Siemens Networks (NSN). From 2005 to 2007, he fundraised and worked as a manager for the Danish-funded project COMET at AAU. From 2002 to 2005, he worked as a research assistant at AAU on two FP5 projects (STRIKE and VeRT) and one industrial project (JADE) in collaboration with the Global Standards & Research Team, Samsung Electronics, Korea.
He is author of the first edition of the book Mobile Positioning and Tracking: from Conventional to Cooperative Techniques (John Wiley & Sons Inc., June 2010). He is author/co-author of more than 65 publications, including papers published in journals, magazines and proceedings of international conferences, book chapters, encyclopedia papers and technical reports. He is inventor/co-inventor of one US patent and four Danish patent applications. He has served as a reviewer for several technical and IPR journals (including the Oxford Journal of Intellectual Property Law & Practice), magazines and international conferences, and as a guest editor for several special issues in various technical journals and magazines. He has been an instructor for a half-day tutorial on wireless location at IEEE PIMRC'07 as well as for seminars and tutorials on IPR at MobileHCI'15 and GWS'14.
He was co-founder of Kyranova Ltd, and co-founder and president of the International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), the International Conference on Cognitive Radio and Advanced Spectrum Management (CogART) and One2One (Business & Science Match).
He is an editorial board member of the journal Recent Advances in Communications and Networking Technology (formerly Recent Patents in Telecommunications, Benthamscience Publishers). He has been a board member of IPR Nord, Chairman of the Danish Section of IEEE Graduates of the Last Decade (GOLD) and a member of the IEEE Aerospace & Electronic Systems Society.
His research interests include (but are not restricted to) cooperation in wireless networks, link layer techniques, wireless location, quality of service mechanisms, next-generation wireless services and architectures, user perspectives and sociological dimensions related to the evolution of technology and society.
Francescantonio Della Rosa received an MSc degree in electrical and electronic engineering from Aalborg University, Denmark, a BSc degree in telecommunications engineering from the University of Cassino, Cassino, Italy, and is a PhD candidate at Tampere University of Technology, Tampere, Finland.
He is an accredited business coach for the European Commission at Horizon 2020 SME Instrument, also serving as chairman at Ekin Labs Oy (Finland). Currently he is the managing director of Technological Innovation at Radiomaze Inc (California), funded by Singularity University Labs and selected to solve the Global Grand Challenges for Humanity in the Security sector at NASA Ames Research Park in Mountain View (USA).
He has successfully coached and instructed more than 20 technology-based ventures, turning research ideas and results into products and businesses.
Francescantonio served as IEEE Finland Section Executive Board Officer and as Honorary Jury at CES 2017 for Space and UAV Category. He funded and managed multi-million euro projects focusing on the commercialization of innovative solutions for business, security, IoT, the space industry, big data, artificial intelligence, such as the EU FP7 Multi-technology Positioning Professionals Marie Curie and the European FP7 project GRAMMAR (Galileo Ready Advanced Mass Market Receiver), leading the team who built the first Galileo receiver for the mass market. He has also commercialization research and development results and the Watchdog project, realizing the first home security solution that can detect a human presence based on the radio wave fluctuations in conventional wifi systems available in domestic environments.
He is the winner of many of international research, innovation and business awards as a result of the commercialization of his research results, for example the CES 2015 Innovation Award Honoree, Las Vegas, Best European Startup 2015 in the Smart Spaces category at EIT Digital, Best Technology Transfer Awards Hipeac Network of Excellence and the NOKIA Foundation Award. He also gained an Entrepreneurial Achievement award from Kauffman FastTrac TechVenture, which grants the bearer the right and responsibility to build an “uncommon company”, and received the nomination as Young Research Entrepreneur of the Year 2016 in Finland and the Honorary Technical Creativity and Business Award from Tampere City (Finland).
He is co-author and editor of three books and many chapters, patents and scientific publications focusing his research interests of positioning and navigation, GNSS, location-based services, big data, IoT, wireless communications, artificial intelligence, business and innovation.
List of Contributors
Gilberto Berardinelli
Aalborg University, Denmark
Tanveer Bhuiyan
Aalborg University, Denmark
Guido Bruck
Lehrstuhl für KommunikationsTechnik, Universität Duisburg-Essen, Germany
Davide Dardari
University of Bologna, Italy
Francescantonio Della Rosa
Radiomaze Inc., Cupertino, USA, Tampere University of Technology, Finland
Simone Frattasi
Sony Mobile Communications, Sweden
João Figueiras
Aalborg University, Denmark
Ismail Guvenc
Wireless Access Laboratory, DOCOMO Communications Laboratories, Palo Alto, CA, USA
Peter Jung
Lehrstuhl für KommunikationsTechnik, Universität Duisburg-Essen, Germany
Nicola Marchetti
CTVR, Trinity College Dublin, Ireland
Jari Nurmi
Tampere University of Technology, Finland
Mauro Pelosi
Radiomaze Inc, Cupertino, California
Andreas Waadt
Lehrstuhl für KommunikationsTechnik, Universität Duisburg-Essen, Germany
Preface
Localization is a research topic that is receiving increasing attention from both academia and industry. Previously considered as vital information for vehicle tracking and military strategy, location information has now been introduced into wireless communication networks. In contrast to dedicated solutions, such as the global positioning system (GPS), that were designed to simply provide positioning information, the new solutions for wireless networks are able to supply the combined benefit of both communication and positioning. As a consequence, the network operator, as well as the service provider and the end user, can profit from such position-enabled communication capabilities. Indeed, while the network operator is able to manage the resources of its network more efficiently, the service provider is able to offer location-based services to the end user, who can fully enjoy such personalized location-dependent services. In particular, it can be found from the literature that location information is being used as a basic requirement for the deployment of new protocols (e.g., routing and clustering), new technologies (e.g., cooperative systems) and new applications (e.g., navigation and location-aware advertising). From the point of view of the industry, the use of location information has been stimulated mainly by applications such as navigation, location-dependent searching and social networking. Since wireless communication networks are nowadays present anywhere and anytime, every location-dependent networking enhancement, service or application can be spread rapidly and used globally.
The above-mentioned trends are a major stimulator for the development of novel solutions for obtaining positioning information in wireless networks. Chapter 1 outlines the motivation behind these solutions and presents potential categories and applications of location-based services (both conventional and network-related).
Chapter 2 introduces the main application areas for positioning, providing an overview of the localization ecosystem and its usability with a look at the main patent trends. Chapter 3 presents the fundamentals of wireless communications for positioning, describing the main radio propagation characteristics of both conventional and cooperative. Chapter 4 presents the fundamentals of positioning, proposing a classification of positioning methods, techniques and main error sources. Chapter 5 describes these various types of data association algorithms, showing the advantages and disadvantages of each. Chapter 6 deals with the fundamentals of tracking, in particular several mobility models (including group-based and socially based models) that are used in the following chapters will be introduced. Chapter 7 considers some advanced techniques (from the realm of signal processing) used to mitigate the errors mentioned in previous chapters, thus trying to enhance the accuracy of the overall location estimation process. Chapter 8 presents the state of the art of satellite-based and terrestrial based positioning systems, spanning the range from outdoor to indoor environments, from wide-area networks to short-range networks, and from orthogonal frequency division multiplexing to ultra-wideband (UWB) technologies. In Chapter 9 we introduce the topic of UWB positioning by describing fundamentals about regulations and positioning approaches for tracking targets. Chapter 10 presents indoor positioning approaches in wireless local area networks by highlighting the effect the environmental impairments and human body signal absorption have on signal strength measurements. Chapter 11 introduces the topic of multi-tag localization by adopting radio frequency identification systems and experimental activities as well. Replicating cooperative human behavior in wireless communications has resulted in a number of emerging research fields. In particular, its application in wireless location has flown in a new breed of techniques that may revolutionize the entire field. Hence, in Chapter 12 we take a tour through the state of the art of what we call “cooperative augmentation systems”, that is, mobile positioning systems that exploit the cooperation of users, terminals and networks to boost their location estimation accuracy in both simulated and real environments.
Acknowledgements
The authors would like to thank the direct contributors to the book, namely, João Figueiras, Gilberto Berardinelli, Nicola Marchetti, Andreas Waadt, Guido Bruck, Peter Jung, Tanveer Bhuiyan, Davide Dardari, Jari Nurmi, Mauro Pelosi, Ismail Guvenc, Rasmus Olsen, Hanane Fathi, Basuki Priyanto, and the indirect contributors, who, in one way or another, have been involved in several of the activities that provided the knowhow for writing the book. Finally, the authors would like to thank the Wiley team, who offered them their unceasing help in order to make this book a reality: Sandra Grayson, Tiina Wigley, Preethi Belkese, Teresa Netzler and Stephan Schwindke.
List of Abbreviations
1L-DF
one-level data fusion
2D
two-dimensional
2L-DF
two-level data fusion
3D
three-dimensional
3G
third generation
3GPP
Third Generation Partnership Project
4G
fourth generation
5G
fifth generation
ACPS
Ad-Coop Positioning System
A-GNSS
Assisted Global Navigation Satellite System
A-GPS
Assisted Global Positioning System
ACK
acknowledge
ADC
analog-to-digital converter
AOA
angle of arrival
AP
access point
API
ppplication programming interface
ASP
application service provider
AWGN
additive white Gaussian noise
B2B
business-to-business
B2C
business-to-consumer
BCCH
broadcast control channel
BF
beamforming
BPM
burst position modulation
BPSK
binary phase shift keying
BPZF
band-pass zonal filter
BS
base station
BTS
base transceiver station
CA
collision avoidance
CAS
cooperative augmentation system
CATV
cable television
CD
collision detection
CDF
cumulative distribution function
CDMA
code division multiple access
CEP
circular error probability
CH
cluster head
CID
cell ID
CG
cluster gateway
CIR
carrier-to-interference ratio
CLI
caller location information
CLS
constrained least squares
CM
cluster member
COFDM
coded orthogonal frequency division multiplexing
COMET
Cooperative Mobile Positioning System
coop-EKF
cooperative extended Kalman filter
coop-WNLLS
cooperative weighted nonlinear least squares
CR
cognitive radio
CRC
cyclic redundancy check
CRLB
Cramér–Rao lower bound
CRB
Cramér-Rao bound
CRMSE
cooperative root mean square error
CSMA
carrier sense multiple-access
CS-MNS
clock sampling–mutual network synchronization
CSN
connectivity service network
CTM
current transformation matrix
CTS
clear to send
CW
continuous wave
DAC
digital-to-analog converter
DAA
detection and avoidance
DGPS
Differential Global Positioning System
DL
downlink
DOP
dilution of precision
DP
direct path
DR
dead reckoning
DS
direct sequence
DSSS
direct-sequence spread spectrum
E911
enhanced 9-1-1
eCall
emergency call
ED
energy detector
EDGE
enhanced data rates for GSM evolution
EGNOS
European Geostationary Navigation Overlay Service
EIRP
effective isotropic radiated power
EKF
extended Kalman filter
e.m.
electromagnetic
EM
expectation maximization
EPS
evolved packet system
ERP
equivalent radiated power
EUWB
European ultra-wideband
FBMC
filter bank multicarrier
FCC
Federal Communications Commission
FDD
frequency division duplex
FDMA
frequency division multiple access
FEC
forward error correction
FFT
fast Fourier transform
FHSS
frequency-hopping spread spectrum
FIM
Fisher information matrix
FRP
fixed reference point
FS
fixed station
FT
fixed terminal
G-CRLB
generalized Cramer–Rao lower bound
GAGAN
GPS-aided Geo-augmented Navigation System
GDOP
geometric dilution of precision
GERAN
GSM/EDGE radio access network
GFDM
generalized frequency division multiplexing
GIS
Geographic Information System
GLONASS
Globalnaya Navigationnaya Sputnikovaya Sistema
GP
guard period
GPB
generalized pseudo-Bayesian
GNSS
Global Navigation Satellite System
GPS
Global Positioning System
GSM
Global System for Mobile Communications
GPS-CM
GPS-equipped CM
HazMat
hazardous material
HLOP
hybrid lines of position
HTAP
hybrid TOA/AOA positioning
HTDOA
hybrid TDOA/AOA positioning
IAD
identify and discard
ID
identification
i.i.d.
independent, identically distributed
IEEE
Institute of Electrical and Electronics Engineers
IF
intermediate frequency
IFFT
inverse fast Fourier transform
IMU
inertial mobile unit
IP
Internet Protocol
IPDL
idle period downlink
IPO
interior-point optimization
IR-UWB
impulse radio UWB
IS
idle slot
IT
information technology
KF
Kalman filter
LAC
local area code
LAN
local area network
LBS
location-based service
LCS
location services
LD
laser diode
LDC
low-duty cycle
LEACH
low-energy adaptive clustering hierarchy
LED
light-emitting diode
LLS
linear least squares
LMS
least median of squares
LMU
location measurement unit
LO
local oscillator
LOS
line of sight
LP
local positioning
LRT
likelihood ratio test
LS
least squares
LT
location and tracking
LTE
long-term evolution
LTS
least-trimmed squares
mmW
millimeter wave
MAC
medium access control
MAP
maximum a posteriori
MCC
mobile country code
MF
matched filter
MIMO
multiple-input–multiple-output
MISO
multiple-input–single-output
ML
maximum likelihood
MLE
maximum-likelihood estimator
MMSE
minimum mean square error
MNC
mobile network code
MPP
Mobile Positioning Protocol
MS
mobile station
MSAS
Multifunctional Satellite Augmentation System
MSC
mobile switching center
MSE
mean squared error
MSK
minimum shift keying
MUI
multi-user interference
MUR
multistatic radar
MVNO
mobile virtual network operator
NAV
network allocation vector
NAVSTAR
navigational satellite timing and ranging
NICT
new information and communication technology
NLLS
nonlinear least squares
NLOS
non-line-of-sight
NNSS
Navy Navigation Satellite System
NTP
network time protocol
OBU
on-board unit
OFDM
orthogonal frequency division multiplexing
OFDMA
orthogonal frequency division multiple access
OOB
out of band
OOK
on–off keying
OSI
open system interconnection
OTDOA
observed time difference of arrival
OTDOA-IPDL
observed time difference of arrival–idle period downlink
ppm
part per million
P2P
peer-to-peer
PAM
pulse amplitude modulation
PC
power control
PCR
predictive channel reservation
PDA
personal digital assistant
probability density function
PDP
power delay profile
PF
particle filter
PHY
physical layer
PLMN
public land mobile network
POI
point of interest
PPM
pulse position modulation
PR
pseudo-random
PRF
position reporting frequency
PRN
pseudo-random number
PSAP
public safety answering point
PSD
power spectral density
PSTN
public switched telephone network
PTP
precision time protocol
QAM
quadrature amplitude modulation
QoS
quality of service
QP
quadratic programming
QPSK
quadrature phase shift keying
QZSS
Quasi-Zenith Satellite System
RBF
recursive Bayesian filtering
RF
radio frequency
RFID
radio frequency identification
RLS
recursive least squares
RMS
root mean square
RMSE
root mean square error
RP
reference point
RRC
root raised cosine
RRM
radio resource management
RSS
received signal strength
RSSI
received signal strength indicator
RT
residual test
RT
response time
RTD
relative time difference
RTLS
real-time locating system
RTS
request to send
RTT
round-trip time
RTS
request to send
RV
random variable
RX
receiver
RXLEV
receiver level
SBAS
Satellite-Based Augmentation System
SBS
serial backward search
SD
spatial diversity
SDK
software development kit
SDMA
space division multiple access
SFN
system frame numbers
SIC
self-interference cancellation
SIMO
single-input–multiple-output
SINR
signal-to-interference-plus-noise ratio
SIR
signal-to-interference ratio
SISO
single-input–single-output
SM
spatial multiplexing
SNR
signal-to-noise ratio
SP
service provider
STBC
space-time block codes
STC
space–time coding
S-V
Saleh-Valenzuela
TA
timing advance
TDMA
time division multiple access
TDOA
time difference of arrival
TH
time-hopping
THSS
time-hopping spread spectrum
TOA
time of arrival
TOF
time of flight
TSF
timing synchronization function
TWR
two-way ranging
TX
transmitter
U-TDOA
uplink–time difference of arrival
UE
user equipment
UFMC
universal filtered multicarrier
UHF
ultra-high frequency
UKF
unscented Kalman filter
UL
uplink
UMTS
Universal Mobile Telecommunications System
UTRAN
UMTS terrestrial radio access network
UWB
ultra-wideband
VCO
voltage-controlled oscillator
WAAS
wide area augmentation system
WGS84
World Geodetic System 1984
WiFi[Wi-Fi]
wireless fidelity
WiMAX
Worldwide Interoperability for Microwave Access
WLAN
wireless local area network
WLS
weighted least squares
WNLLS
weighted nonlinear least squares
WPAN
wireless personal area network
WSN
wireless sensor network
WSR
wireless sensor radar
ZZB
Ziv-Zakai bound
Notations
Symbol
Description
,
,
scalar
,
,
vector or matrix
,
,
scalar, vector or matrix
or
with a descriptive label “rel”
transpose of
inverse of
estimator of
mean of
determinant of
with respect to element
with respect to a relation between
and
, both from the same group
with respect to a relation between
and
from a different group
equivalent to
equivalent to
or
at discrete time
at discrete time
compared with time
vector or matrix containing all occurrences of
from discrete time
until
difference between two values of
set of
combinations of a set
probability of
PDF of
normal distribution with mean
and standard deviation
Chapter 1Introduction
Joaõ Figueiras1, Francescantonio Della Rosa2,3 and Simone Frattasi4
1Aalborg University, Aalborg, Denmark
2Radiomaze Inc, Cupertino, California, USA
3Tampere University of Technology, Finland
4Sony Mobile Communications, Lund, Sweden
Over the past few decades, wireless communications have become essential in everyone's daily life. The world has become mobile, and continuous access to information has become a requirement. Owing to this necessity for fresh, real-time, first-hand information, devices such as mobile phones, computers, pagers, data cards, sensors and data chips have been entering our lives as typical technology “buddies”. For this reason, wireless services have gained popularity, and location information has become useful information in the wireless world. The continuous demand for information has created a huge potential for business opportunities and it has promoted innovation towards the development of new services. As a consequence, the infrastructure that enables communication among wireless devices has been rapidly growing towards higher coverage, higher flexibility and higher interoperability. This reality is so visible that it is nowadays unthinkable to envision our lives without these technologies. Furthermore, with the rapid deployment of wireless communication networks, positioning information has become of great interest. Because of the inherent mobility behavior that characterizes wireless communication users, location information has also become crucial in several circumstances, such as rescues, emergencies and navigation. This position dependency has boosted research, development and business around the topic of positioning mechanisms for wireless communication technologies. The result is a wide variety of integrated and built-in solutions that can combine and interoperate with communication and position information. Thus, this book covers the topic of positioning mechanisms for wireless communication technologies. It explains in detail the services, wireless communication protocols, positioning and tracking algorithms, error mitigation techniques, implementations used in wireless communication systems, and the most recent techniques of cooperative positioning.
Positioning or location can be understood as the unambiguous placement of a certain individual or object with respect to a known reference point. This reference point is often assumed to be the center of the Earth coordinate system. In practice, the reference point can be any point on the Earth1 that is known to the system and which all the coordinates can relate to. Although the position itself is obviously a very important source of information, this position must be related to a specific time to be even more useful. In particular, when we consider tracking systems, time information is a key necessity, not only for knowing the position of a certain device at a specific time, but also for inferring higher-order derivatives of the position, that is, speed and acceleration. Thus, “tracking” is a method for estimating, as a function of time, the current position of a specific target. “Navigation” is a tracking solution that aims primarily at using position information in order to help users to move towards a desired destination.
Although enabling position estimation with communication technologies is currently a hot topic, the necessity for determining the position of individuals, groups, animals, vehicles or any type of object is an ancient necessity that can be seen as a basic need. This necessity has been present in human life for many centuries and it is so important that humans and animals have in-built biological mechanisms that permit individuals to localize and orient themselves in many situations. When we consider the actual methods for obtaining position information, the history is long and the systems are numerous. During the early years, a few millennia ago, orientation and positioning were already possible using devices that resemble present-day magnetic compasses, using maps of sea currents and winds, or using celestial navigation techniques. For centuries, celestial navigation, by means of observations of the positions of stars and the Sun, was the most important technique for estimating position information. By knowing, for instance, the position of the Sun or well-known star constellations, people were able to determine their orientation and navigate on the Earth. This technique is so important that it is still used today to provide a rough sense of one's orientation when no other tool is available. Although the mechanisms for orientation based on star position readings and compass readings have been used for at least two millennia, these mechanisms were widely used and improved during the period of sea exploration in the 15th and 16th centuries. This was an important period in the history of positioning systems. Several objects, such as the cross-staff and astrolabe (and, later, the quadrant and the sextant, invented in the 17th and 18th centuries), permitted sea explorers to read the position of the stars and subsequently calculate their own position, often supported by the incomplete maps that were available at that time. Associated with these tools were other techniques for predicting and inferring future positions based on the analysis of past movements. These techniques were often used to navigate when, for instance, the sky was not clear and they were generally complemented by anchor points of known location, such as points on the shore. In the 18th century, the chronometer was invented. This tool, widely used in ship navigation, permitted calculations of position to be connected more efficiently to corresponding timestamps. By the end of the 19th century, wireless communications emerged and, along with this remarkable discovery, the first position solutions based on electromagnetic waves started to be developed. This period marked an important turn not only in the history of positioning systems, but also in the entire history of communication technology. The first positioning system to be invented was the radio direction finder, a device which was able to determine the direction from where radio waves were being generated. The basic concept of this device was to find the null (i.e., the direction which results in the weakest signal) in the signal observed with a directional antenna mounted on a portable support. Only by the mid-20th century were the first radars invented. Ever since, these systems have been used and enhanced, and they are still widely used for several positioning purposes. Radar is a system that transmits radio waves towards a target and is able to read the signals that are received back after they have been reflected from the target itself. From this period onwards, great development in positioning systems has occurred, culminating in the wide variety of systems currently available. One of the most famous systems is the long-range navigation (LORAN) system, proposed in 1940, which was characterized by beacons radiating synchronized signals that were then read by target receivers. The receivers had to be able to measure time differences in the arrival of the signals in order to calculate their positions. Later on, by the 1960s, satellite positioning systems started to be deployed, and space exploration began. Currently, Global Positioning System (GPS) is the best known and most used satellite positioning system. Over the past 30–40 years, several other positioning solutions have been deployed for use in various scenarios, based on various approaches, using infrared, ultrasound, image processing or electromagnetic waves.
In parallel with the deployment of positioning and communication systems during the last decade, research has evolved in the direction of integrating communication and positioning into a single system. Some examples are the current standards for the Third Generation Partnership Project (3GPP) and ultrawideband (UWB), which include information about positioning mechanisms in the communication specifications. The combination of these functionalities has leveraged new services, namely location-based services (LBSs). In contrast to the earlier dedicated solutions that were designed to simply provide positioning, the new solutions for wireless networks are able to provide the combined benefit of both communication and positioning. As a consequence, the whole network, as well as the end user and the service providers, can benefit from position-enabled communication capabilities: the network operator can manage all the network's resources in a more efficient way, the service provider is able to deliver new services based on the user's position, and the user can enjoy new personalized, location-dependent services. Many of the services proposed in research documents assume location information as a basic requirement for deployment of new protocols (e.g., routing and clustering), new technologies (e.g., cooperative systems) and new applications (e.g., navigation and location-aware advertising). Furthermore, in the industrial environment, location information has also been widely used in applications such as navigation, location-dependent searching and social networks. Since wireless communication networks are nowadays almost present anywhere at any time, any new location-dependent networking enhancement, service or application can be spread rapidly and used globally.
The location information mechanisms in wireless communication technologies can be classified into positioning systems and positioning solutions, as shown in Figure 1.1. A positioning system concerns the hardware and software necessary to measure the properties of wireless links and to subsequently process those measurements in order to estimate the user's position. Positioning systems are typically integrations of entire systems into communication technologies, such as the GPS integration into mobile phones and cellular base stations (BSs). In contrast, a positioning solution is typically a software-based implementation, where indirect measurements of the user's position are obtained in an opportunistic fashion from the adaptation of mechanisms existing in communication technologies. The enablement of location information in current wireless communication networks can be done either by integrating a positioning system into the network or by implementing a positioning solution that extracts location information, thus exploiting the potential of the network. By integrating a positioning system into the network, it is usually possible to obtain better accuracy than what is obtained by a direct implementation in the network. The disadvantage is that integrating additional
