Nurmi / Lohan / Wymeersch Multi-Technology Positioning

1. Auflage 2017
ISBN: 978-3-319-50427-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 345 Seiten

This book provides an overview of positioning technologies, applications and services in a format accessible to a wide variety of readers. Readers who have always wanted to understand how satellite-based positioning, wireless network positioning, inertial navigation, and their combinations work will find great value in this book. Readers will also learn about the advantages and disadvantages of different positioning methods, their limitations and challenges. Cognitive positioning, adding the brain to determine which technologies to use at device runtime, is introduced as well. Coverage also includes the use of position information for Location Based Services (LBS), as well as context-aware positioning services, designed for better user experience.


Jari Nurmi works as a Professor at Tampere University of Technology, Finland since 1999, in the Faculty of Computing and Electrical Engineering. He is working on embedded computing systems, wireless localization, positioning receiver prototyping, and software-defined radio. He held various research, education and management positions at TUT since 1987 and was the Vice President of the SME VLSI Solution Oy 1995-1998. Since 2013 he is also a partner and co-founder of Ekin Labs Oy, a research spin-off company commercializing technology for human presence detection, now headquartered in Silicon Valley as Radiomaze, Inc. He has supervised 19 PhD and over 130 MSc theses at TUT, and been the opponent or reviewer of 32 PhD theses for other universities worldwide. He is a senior member of IEEE, member of the technical committee on VLSI Systems and Applications at IEEE CAS, and board member of Tampere Convention Bureau. In 2011 he received IIDA Innovation Award, and in 2013 the Scientific Congress Award and HiPEAC Technology Transfer Award. He is a steering committee member of four international conferences, in the chairman position in two. He has edited 5 Springer books, and has published about 350 international conference and journal articles and book chapters. He has participated in the Marie Curie ITN network MULTI-POS as the network coordinator and scientist in charge.
Elena-Simona Lohan is an Associate Professor at the Department of Electronics and Communication Engineering at Tampere University of Technology (TUT). She is also a Visiting Professor at Universitat Autònoma de Barcelona, Spain. She received an M.Sc. degree in Electrical Engineering from Polytechnics University of Bucharest, Romania, in 1997, a D.E.A. degree (French equivalent of master) in Econometrics, at ´ Ecole Polytechnique, Paris, France, in 1998, and a Ph.D. degree in Telecommunications from TUT, in 2003. She has more than 170 international publications, 3 patents and 2 patent applications. She is serving as Associate Editor to IET Radar, Sonar and Navigation journal and to RIN Cambridge Journal of Navigation since 2013. Her current research interests include wireless location techniques based on signals of opportunity and cognitive spectrum sensing for positioning purposes. She has participated in the Marie Curie ITN network MULTI-POS as scientist in charge and equality officer.
Henk Wymeersch is a Professor in Communication Systems with the Department of Signals and Systems at Chalmers University of Technology, Sweden. Prior to joining Chalmers, he was a postdoctoral researcher from 2005 until 2009 with the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Henk Wymeersch obtained the Ph.D. degree in Electrical Engineering/Applied sciences in 2005 from Ghent University, Belgium. He served as Associate Editor for IEEE Communication Letters (2009-2013), IEEE Transactions on Wireless Communications (since 2013), and IEEE Transactions on Communications (since 2016). His current research interests include cooperative systems and intelligent transportation. He has participated in the Marie Curie ITN network MULTI-POS as scientist in charge.
Gonzalo Seco-Granados received the PhD degree on Telecommunications Engineering from Universitat Politècnica de Catalunya in 2000 and an MBA from IESE in 2002. From 2002 to 2005, he was member of the technical staff the European Space Agency, involved in the design of the Galileo system. Since 2006, he is Associate Professor at the Department of Telecommunications, Universitat Autònoma de Barcelona. He has been principal investigator of over 25 research projects. In 2014, he received an ICREA Academia fellowship. In 2015, he wasFulbright Visiting Scholar at University of California, Irvine. His research interests include the design of signals and reception techniques for satellite-based and terrestrial positioning systems, multi-antenna receivers and signal-level integrity. He has participated in the Marie Curie ITN network MULTI-POS as scientist in charge.
Ossi Nykänen works for M-Files, helping enterprises find, share, and secure documents and information. Dr. O. Nykänen also serves as an Adjunct Professor at Tampere University of Technology, Department of Mathematics. His long-term research interests include semantic computing, machine learning, information modeling and visualization, (computer-supported) mathematics, and the related applications. He is affiliated with many industrial and academic research networks, and is an advocate of international web standards. He has participated in the Marie Curie ITN network MULTI-POS as scientist in charge.

Autoren/Hrsg.

Nurmi, Jari

Lohan, Elena-Simona

Wymeersch, Henk

Seco-Granados, Gonzalo

Nykänen, Ossi

Inhaltsverzeichnis

1;Preface;5
2;Acknowledgements;7
3;Contents;8
4;1 Introduction and Book Structure;10
4.1;References;13
5;2 MULTI-POS: Multi-Technology Positioning Professionals Training Network;14
5.1;2.1 How Everything Has Started;14
5.2;2.2 MULTI-POS Aims and Structure;15
5.3;2.3 Main Results and a Look Ahead;18
6;3 Understanding the GNSS Signal Model;21
6.1;3.1 Introduction;21
6.2;3.2 Signal Model Basics;22
6.2.1;3.2.1 Passband Signal Model;23
6.2.2;3.2.2 Baseband Signal Model;26
6.3;3.3 Signal-to-Noise and Carrier-to-Noise Density Ratios;31
6.4;3.4 Noise in Computer Simulation;35
6.4.1;3.4.1 From Baseband to Passband and Back;37
6.4.2;3.4.2 Noise Correlation;41
6.4.3;3.4.3 Simulation Setup;44
6.5;3.5 The Doppler Effect;45
6.5.1;3.5.1 The Doppler Effect Fundamentals;46
6.5.1.1;3.5.1.1 Static Transmitter and Moving Receiver;47
6.5.1.2;3.5.1.2 Moving Transmitter and Static Receiver;49
6.5.1.3;3.5.1.3 Considering the Special Relativity;51
6.5.2;3.5.2 Practical Values of the Doppler Shift in gnss;53
6.5.3;3.5.3 Impact of the Doppler Effect on the Received Signal;56
6.5.4;3.5.4 gnss Signal Model Revision;57
6.6;3.6 Conclusions;59
6.7;References;60
7;4 GNSS Vulnerabilities;62
7.1;4.1 Introduction and Motivation;62
7.2;4.2 Ionospheric Error;65
7.2.1;4.2.1 What is the Ionospheric Error?;65
7.2.2;4.2.2 Ionospheric Error Mitigation;66
7.2.2.1;4.2.2.1 Dual Frequency Combination;66
7.2.2.2;4.2.2.2 Physics-Based Data Driven Ionospheric Models;66
7.2.2.3;4.2.2.3 Ionospheric Maps;66
7.2.2.4;4.2.2.4 Ionospheric Data Driven Models;67
7.3;4.3 Multipath;67
7.3.1;4.3.1 Impact of Multipath on Code Measurements;69
7.3.2;4.3.2 Impact of Multipath on Carrier Phase Measurements;69
7.3.3;4.3.3 Impact of Multipath on Signal Strength and Doppler Frequency Measurements;70
7.3.4;4.3.4 System Sensitivity to Multipath;71
7.3.5;4.3.5 Multipath Mitigation;71
7.3.5.1;4.3.5.1 Correlation-Based Mitigation;71
7.3.5.2;4.3.5.2 Measurement-Based Mitigation;72
7.3.5.3;4.3.5.3 Hardware Mitigation;72
7.3.5.4;4.3.5.4 Other Types of Mitigation;73
7.3.6;4.3.6 Multipath Detection Without Mitigation;73
7.4;4.4 Radio Frequency Interference in GNSS;73
7.4.1;4.4.1 Unintentional RF Interference;74
7.4.1.1;4.4.1.1 Commercial Broadcasting Interference;74
7.4.2;4.4.2 Intentional RF Interference;76
7.4.2.1;4.4.2.1 Jamming;76
7.4.2.2;4.4.2.2 Spoofing;77
7.4.3;4.4.3 Interference Countermeasures;77
7.4.3.1;4.4.3.1 Interference Detection;78
7.4.3.2;4.4.3.2 Interference Mitigation;79
7.5;4.5 Conclusions;80
7.6;References;81
8;5 GNSS Quality of Service in Urban Environment;85
8.1;5.1 Conventional gnss Signal Tracking;85
8.1.1;5.1.1 Code Tracking Loop;88
8.1.2;5.1.2 Carrier Tracking Loop;91
8.2;5.2 Problematic in Urban Environment;93
8.3;5.3 Advanced Signal Processing;94
8.3.1;5.3.1 gnss nlos Rejection Technique;94
8.3.2;5.3.2 vt Technique;95
8.3.2.1;5.3.2.1 vdfll ekf State Model;97
8.3.2.2;5.3.2.2 vdfll ekf Observation Model;99
8.3.2.3;5.3.2.3 vdfll Estimation Workflow;100
8.3.2.4;5.3.2.4 Performed Tests in Urban Conditions;101
8.4;5.4 Carrier Phase Measurements in Urban Environments;104
8.5;5.5 Conclusions;109
8.6;References;110
9;6 Multi-GNSS: Facts and Issues;112
9.1;6.1 Introduction;112
9.2;6.2 Global Navigation Satellite Systems;113
9.2.1;6.2.1 GPS;113
9.2.1.1;6.2.1.1 Space Segment;114
9.2.1.2;6.2.1.2 Current and Planned Signals;114
9.2.1.3;6.2.1.3 Time and Geodetic Reference Frame;116
9.2.2;6.2.2 GLONASS;117
9.2.2.1;6.2.2.1 Space Segment;118
9.2.2.2;6.2.2.2 Current and Planned Signals;118
9.2.2.3;6.2.2.3 Time Scale and Geodetic Reference Frame;119
9.2.3;6.2.3 Galileo;120
9.2.3.1;6.2.3.1 Space Segment;121
9.2.3.2;6.2.3.2 Current and Planned Signals;121
9.2.3.3;6.2.3.3 Time Scale and Geodetic Reference Frame;122
9.2.4;6.2.4 BeiDou;123
9.2.4.1;6.2.4.1 Space Segment;124
9.2.4.2;6.2.4.2 Current and Planned Signals;124
9.2.4.3;6.2.4.3 Time Scale and Geodetic Reference Frame;125
9.3;6.3 Multi-GNSS Benefits;125
9.4;6.4 Multi-GNSS Issues;126
9.5;6.5 Conclusions;127
9.6;References;128
10;7 Towards Seamless Navigation;130
10.1;7.1 Introduction and Motivation;130
10.2;7.2 Adaptability;131
10.2.1;7.2.1 Configurational Flexibility;131
10.2.2;7.2.2 Environmental Adaptability;132
10.3;7.3 Context;133
10.3.1;7.3.1 Location;133
10.3.2;7.3.2 Behaviour;134
10.4;7.4 Sensors;135
10.4.1;7.4.1 Maps and Infrastructure;136
10.4.2;7.4.2 Odometers and Tactile Sensors;137
10.4.3;7.4.3 Sound and Pressure;138
10.4.4;7.4.4 Inertial Sensors;139
10.4.5;7.4.5 GNSS, Pseudolite and Broadcast Signals;140
10.4.6;7.4.6 WiFi, Bluetooth and Ultra-Wideband;141
10.4.7;7.4.7 Magnetic Sensors;142
10.4.8;7.4.8 Visible and Infrared Light Sensors;143
10.5;7.5 Sensor Fusion;145
10.5.1;7.5.1 Estimation;146
10.5.2;7.5.2 Classification;147
10.5.3;7.5.3 Inference;148
10.6;7.6 Conclusions;149
10.7;References;150
11;8 Mapping the Radio World to Find Us;153
11.1;8.1 Introduction;153
11.1.1;8.1.1 Opportunities;154
11.1.2;8.1.2 Overview;155
11.2;8.2 From Power to Distance;156
11.2.1;8.2.1 Non-free Space Loss;156
11.2.2;8.2.2 Propagation Impairments;156
11.2.3;8.2.3 ITU-R Model;157
11.2.4;8.2.4 Log-Distance Model;158
11.2.5;8.2.5 Typical Values;158
11.3;8.3 Fingerprinting;160
11.3.1;8.3.1 Learning Phase;160
11.3.1.1;8.3.1.1 Building the learning database;160
11.3.2;8.3.2 Online Phase;163
11.3.3;8.3.3 A Simple Example;163
11.3.4;8.3.4 Shortcomings;165
11.3.4.1;8.3.4.1 Consistency;165
11.3.4.2;8.3.4.2 Storage;165
11.3.4.3;8.3.4.3 Privacy;166
11.3.4.4;8.3.4.4 Hardware;166
11.4;8.4 Conclusions;166
11.5;References;167
12;9 Survey on 5G Positioning;169
12.1;9.1 Introduction;169
12.2;9.2 The Relation Between 5G and Cognitive Radio for Localization;172
12.3;9.3 On 5G Systems;172
12.3.1;9.3.1 Mm-Wave;172
12.3.2;9.3.2 Massive MIMO;173
12.3.3;9.3.3 Device-Centric Architecture;173
12.3.3.1;9.3.3.1 Device-Centric and Cell-Centric Positioning;175
12.3.4;9.3.4 D2D Communication;175
12.3.4.1;9.3.4.1 Device Relaying Communication with Base Station Controlled Link;175
12.3.4.2;9.3.4.2 Direct Device-to-Device Communication with Base Station Controlled Link;175
12.3.4.3;9.3.4.3 Device Relaying Communication with Device Controlled Link;175
12.3.4.4;9.3.4.4 Direct Device-to-Device Communication with Device Controlled Link;176
12.3.5;9.3.5 Location-Aware Communications;176
12.3.6;9.3.6 Ultra Dense Networks;177
12.3.6.1;9.3.6.1 Mobility Management in Ultra Dense Networks;177
12.4;9.4 Mm-Wave Channels;177
12.4.1;9.4.1 Path-Loss;178
12.4.1.1;9.4.1.1 Frequency Dependent Path-Loss;178
12.4.1.2;9.4.1.2 Geometry Based Statistical Path-Loss;178
12.4.2;9.4.2 Mm-Wave mimo Channel Model;179
12.4.3;9.4.3 Parameter Estimation;181
12.4.3.1;9.4.3.1 SAGE;181
12.4.3.2;9.4.3.2 RIMAX;181
12.4.4;9.4.4 Sparsity;181
12.5;9.5 Multi-Beam Transmission;184
12.5.1;9.5.1 Hybrid Beamformers;185
12.5.2;9.5.2 Beam Training Protocols;186
12.6;9.6 Localization Based on Delay, AOA, and AOD;187
12.6.1;9.6.1 General Localization Techniques;187
12.6.1.1;9.6.1.1 Localization Using Range Measurements;187
12.6.1.2;9.6.1.2 Localization Using Range-Difference Measurements;188
12.6.1.3;9.6.1.3 Triangulation;189
12.6.1.4;9.6.1.4 Fingerprinting;190
12.6.2;9.6.2 Mm-Wave Localization Techniques;190
12.7;9.7 Simulation Results;192
12.7.1;9.7.1 Simulation Setup;193
12.7.2;9.7.2 Results and Discussion;194
12.8;9.8 Conclusions;195
12.9;References;198
13;10 Formation Control of Multi-Agent Systems with Location Uncertainty;201
13.1;10.1 Introduction;201
13.2;10.2 Localisation for Communication and Control;203
13.2.1;10.2.1 Inter-Agent Communication;203
13.2.2;10.2.2 Multi-Agent Control;204
13.2.3;10.2.3 Bounding Uncertainty of Estimators: The Cramér-Rao Bound;204
13.3;10.3 Impact of Location Uncertainty on Mission Goals;205
13.3.1;10.3.1 Limiting Location Uncertainty;205
13.3.1.1;10.3.1.1 Problem Formulation;205
13.3.1.2;10.3.1.2 Optimisation Formulation;207
13.3.1.3;10.3.1.3 Performance Evaluation;207
13.3.2;10.3.2 Location-Aware Formation Control;208
13.3.2.1;10.3.2.1 Problem Formulation;208
13.3.2.2;10.3.2.2 Optimisation Formulation;210
13.3.2.3;10.3.2.3 Performance Evaluation;212
13.4;10.4 Impact of Location Uncertainty on Channel Gain Prediction;213
13.4.1;10.4.1 Problem Formulation;213
13.4.2;10.4.2 Channel Prediction;214
13.4.3;10.4.3 Performance Evaluation;215
13.4.3.1;10.4.3.1 Learning;215
13.4.3.2;10.4.3.2 Prediction;216
13.5;10.5 Conclusion;217
13.6;References;217
14;11 Positioning Technology Applications Relatedto Environmental Issues;220
14.1;11.1 Introduction;220
14.2;11.2 Definitions;220
14.3;11.3 Environmental Issues;222
14.4;11.4 Geographic Information Systems;225
14.5;11.5 Example of Environmental Applications of the Satellite Remote Sensing;227
14.5.1;11.5.1 Land Monitoring;227
14.5.2;11.5.2 Ocean and Coastal Zones;228
14.5.3;11.5.3 Biodiversity;229
14.5.4;11.5.4 Telemetry Techniques for Monitoring Animals;230
14.5.4.1;11.5.4.1 GPS Tracking;231
14.5.4.2;11.5.4.2 Argos Satellite Tracking;231
14.5.4.3;11.5.4.3 Very High Frequency Radio Tracking;231
14.5.4.4;11.5.4.4 Acoustic Tracking;232
14.5.4.5;11.5.4.5 Passive Integrated Transponder;232
14.5.4.6;11.5.4.6 Combination of Tracking Technologies;232
14.5.5;11.5.5 Illegal Fishing;232
14.5.6;11.5.6 Poaching Combat;233
14.5.7;11.5.7 Dealing with Natural Hazards;234
14.5.8;11.5.8 Marine Debris;234
14.6;11.6 European Space Agency: Earth Observation Programs;235
14.7;11.7 The COPERNICUS Program;238
14.7.1;11.7.1 Satellite Equipment and Contributing Missions;239
14.7.2;11.7.2 Sentinels;242
14.7.3;11.7.3 Copernicus Data Access;247
14.8;11.8 Advantages and Limitation of GIS and RS;247
14.9;11.9 Summary;248
14.10;References;249
15;12 Context Awareness for Semantic Mobile Computing;253
15.1;12.1 Introduction;253
15.1.1;12.1.1 Motivation;253
15.1.2;12.1.2 Background;254
15.2;12.2 Context Engine;255
15.2.1;12.2.1 Architecture;255
15.2.2;12.2.2 Responsibilities;256
15.2.3;12.2.3 Context Ontology;256
15.2.4;12.2.4 Context Queries;257
15.3;12.3 Methods for Acquiring Contextual Attributes;259
15.3.1;12.3.1 Case 1: Inferring User Location Based on Phone Usage;259
15.3.1.1;12.3.1.1 Dataset;259
15.3.1.2;12.3.1.2 Data Processing;260
15.3.1.3;12.3.1.3 Methods and Results;261
15.3.2;12.3.2 Context Inference in Social Network Settings;261
15.3.2.1;12.3.2.1 Quantifying Homophily;263
15.3.2.2;12.3.2.2 Using Homophily to Improve Predictions;264
15.3.2.3;12.3.2.3 Experiment;265
15.4;12.4 Applications Areas;266
15.5;12.5 Conclusions;267
15.6;References;268
16;13 The Impact of Galileo Open Service on the Location Based Services Markets: A Review on the Cost Structure and the Potential Revenue Streams;270
16.1;13.1 Introduction;270
16.2;13.2 Galileo Cost Structure and Skepticism;272
16.3;13.3 Galileo Potential Revenue Streams and Opportunities;274
16.4;13.4 Conclusions;281
16.5;References;281
17;14 Location Based Services Analysis Through Analytical Hierarchical Processes: An e-Health-Based Case Study;283
17.1;14.1 Introduction;283
17.1.1;14.1.1 Motivation;283
17.1.2;14.1.2 Key Questions Related to Location Based Services;286
17.1.3;14.1.3 Some Definitions and Introductory Concepts;288
17.2;14.2 What is an Analytical Hierarchical Process?;289
17.3;14.3 Technologies Which Can Be Used for Positioning;291
17.3.1;14.3.1 Wearable Technologies;291
17.3.2;14.3.2 Device-Free Technologies;293
17.3.3;14.3.3 Examples of Indoor Positioning Technologies;294
17.4;14.4 Simplified Example of Applying AHP in the e-Health LBS Context;294
17.5;14.5 Multi-Level and Joint Decisions;297
17.6;14.6 Conclusions;298
17.7;References;298
18;15 DroneAlert: Autonomous Drones for Emergency Response;302
18.1;15.1 Unmanned Air Vehicles;302
18.1.1;15.1.1 Types of uav;302
18.1.2;15.1.2 Laws and Regulations for Unmanned Air Vehicles;304
18.1.2.1;15.1.2.1 Privacy Considerations;304
18.1.2.2;15.1.2.2 Airspace Flight Restrictions;304
18.1.2.3;15.1.2.3 Flying License and Aircraft Registration;305
18.1.3;15.1.3 Current and Future Fields of Application;306
18.1.4;15.1.4 Components;306
18.2;15.2 Emergency Response;308
18.2.1;15.2.1 Sources of Emergency Alerts;308
18.2.2;15.2.2 Types of Emergency Situations;309
18.3;15.3 The DroneAlert System;309
18.3.1;15.3.1 Autonomous uavs;310
18.3.2;15.3.2 UAV Limitations;311
18.4;15.4 System Architecture;312
18.4.1;15.4.1 Components;312
18.4.1.1;15.4.1.1 Emergency Management System;313
18.4.1.2;15.4.1.2 UAV Ground Control System;313
18.4.1.3;15.4.1.3 uav Base Station Operator;314
18.4.1.4;15.4.1.4 uav;314
18.4.1.5;15.4.1.5 Mission Control Center Operator;314
18.4.2;15.4.2 Activities;315
18.4.2.1;15.4.2.1 Request Nearest Available uav;315
18.4.2.2;15.4.2.2 Calculate Optimal Flight Route;315
18.4.2.3;15.4.2.3 Send UAV on Mission;318
18.4.2.4;15.4.2.4 Acquire UAV Telemetry;318
18.4.2.5;15.4.2.5 Send UAV Imagery to Emergency Services;318
18.4.2.6;15.4.2.6 Return UAV from Mission;319
18.5;15.5 Conclusions;319
18.6;References;320
19;16 MULTI-POS: Lessons Learnt from Fellows and Supervisors;321
19.1;16.1 Introduction;321
19.2;16.2 Administrative Issues;322
19.3;16.3 Scientific Issues;324
19.4;16.4 Personal and Cultural Issues;325
19.5;16.5 Summary;327
20;17 Conclusions;328
21;About the Editors;330
22;Acronyms;332
23;Index;338