Array Processing Techniques for Direction of Arrival Estimation, Communications, and Localization

171 Pages·2017·5.82 MB·English

Array Processing Techniques for
Direction of Arrival Estimation,
Communications, and Localization in
Vehicular and Wireless Sensor Networks
Marco Antonio Marques Marinho
Supervisors:
Alexey Vinel
João Paulo Carvalho Lustosa da Costa
Felix Antreich
DOCTORAL THESIS | Halmstad University Dissertations no. 43
Array Processing Techniques for Direction of Arrival Estimation,
Communications, and Localization in Vehicular and Wireless Sensor Networks
© Marco Antonio Marques Marinho
Halmstad University Dissertations no. 43
ISBN 978-91-87045-88-2 (printed)
ISBN 978-91-87045-89-9 (pdf)
Publisher: Halmstad University Press, 2018 | www.hh.se/hup
Aos meus pais:
Marco Antonio e Marcelita, tudo que hÆ de bom em mim nasceu de
vocŒs.AprendimuitoaolongodessesanosdedoutoradolongedevocŒs,
mas a maior li(cid:231)ªo que aprendi Ø vocŒs sªo as pessoas mais importantes
da minha vida. Amo vocŒs acima de tudo.
Ao meu irmªo:
Murilo,companheiroaolongodessajornadaqueescolhemos.Semnos-
sas conversas e discussıes sobre a vida de um doutorando eu provavel-
mente teria perdido a batalha mais importante dessa guerra, a de
mater-me sªo e sensato. Obrigado por tudo.
A minha noiva:
Stephanie, minha parceira e amiga. A dist(cid:226)ncia ao longo desses anos
nªo foi capaz de diminuir seu amor, companheirismo e carinho. Obri-
gado por sua dedica(cid:231)ªo e paciŒncia nos momentos mais dif(cid:237)ceis. Sua
for(cid:231)a em frente as di(cid:28)culdades foi uma grande fonte de inspira(cid:231)ªo. Te
amo.
Marco Antonio Marques Marinho
i

Abstract
Array signal processing in wireless communication has been a topic of inter-
est in research for over three decades. In the fourth generation (4G) of the
wireless communication systems, also known as Long Term Evolution (LTE),
multi antenna systems have been adopted according to the Release 9 of the
3rd Generation Partnership Project (3GPP). For the (cid:28)fth generation (5G) of
the wireless communication systems, hundreds of antennas should be incorpo-
rated to the devices in a massive multi-user Multiple Input Multiple Output
(MIMO) architecture. The presence of multiple antennas provides array gain,
diversitygain,spatialgain,andinterferencereduction.Furthermore,arraysen-
ablespatial(cid:28)lteringandparameterestimation,whichcanbeusedtohelpsolve
problemsthatcouldnotpreviouslybeaddressedfromasignalprocessingper-
spective.Theaimofthisthesisistobridgesomegapsbetweensignalprocessing
theory and real world applications. Array processing techniques traditionally
assume an ideal array. Therefore, in order to exploit such techniques, a robust
set of methods for array interpolation are fundamental and are developed in
this work. In this dissertation, novel methods for array interpolation are pre-
sentedandtheirperformanceinrealworldscenariosisevaluated.Problemsin
the(cid:28)eldofwirelesssensornetworksandvehicularnetworksarealsoaddressed
fromanarraysignalprocessingperspective.Signalprocessingconceptsareim-
plemented in the context of a wireless sensor network. These concepts provide
a level of synchronization su(cid:30)cient for distributed multi antenna communica-
tiontobeapplied,resultinginimprovedlifetimeandimprovedoverallnetwork
behaviour.Arraysignalprocessingmethodsareproposedtosolvetheproblem
of radio based localization in vehicular network scenarios with applications in
road safety and pedestrian protection.
iii

Acknowledgments
To Prof. Felix Antreich, my friend, mentor, and co-supervisor. For helping
me out through the rough patches along the way, not just academically, but
also when got the short end of the stick after physical encounters with motor
vehicles. Without his support and guidance I would not have arrived at this
destination. It is a great honor to work and research alongside him.
To my supervisor Prof. Joªo Paulo Carvalho Lustosa da Costa, to whom I
own so much, for the endless support throughout my entire academic career.
I am thankful for his patience, trust and for the endless opportunities he has
provided me during my academic career. It is thanks to him I have gotten to
experience so much of the world and had the opportunity to know so many
interesting (cid:28)elds of research.
To my supervisor Prof. Alexey Vinel, who deposited so much trust in me
by accepting me as a student in Halmstad. He always did his best and dedi-
cated so much time to ensure I could apply my knowledge to interesting and
relevant topics during my stay in Sweden. I appreciate all his e(cid:27)ort to make
my Ph.D. experience productive and stimulating. Without his help many in-
teresting results would never have come to be.
To Prof. Edison Pignaton de Freitas for his support and incentive across
my academic career. He was always able to keep a good humour even when I
had lost mine. His seamlessly endless ability to moderate con(cid:29)ict and connect
people have help me throughout this journey and serve as a great inspiration.
To Prof. Fredrik Tufvesson for the time dedicated to following my work
and for all the suggestions and guidance o(cid:27)ered.
v

Contents
Introduction 1
1 Array Interpolation 5
1.1 Overview and Contribution . . . . . . . . . . . . . . . . . . . . 5
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.1 Forward Backward Averaging (FBA) . . . . . . . . . . . 13
1.4.2 Spatial Smoothing (SPS) . . . . . . . . . . . . . . . . . 14
1.4.3 Model Order Selection . . . . . . . . . . . . . . . . . . 14
1.4.4 Estimation of Signal Parameters via Rotational Invari-
ance Techniques (ESPRIT) . . . . . . . . . . . . . . . . 15
1.4.5 Vandermonde Invariance Transformation (VIT) . . . . 17
1.5 Array Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.6 Classical Interpolation . . . . . . . . . . . . . . . . . . . . . . . 19
1.7 Sector Selection and Discretization . . . . . . . . . . . . . . . . 23
1.7.1 UT Discretization . . . . . . . . . . . . . . . . . . . . . 26
1.7.2 Principal Component Discretization . . . . . . . . . . . 28
1.8 Linear Adaptive Array Interpolation . . . . . . . . . . . . . . . 29
1.8.1 Data Transformation and Model Order Selection . . . . 32
1.8.2 Linear UT Array Interpolation . . . . . . . . . . . . . . 34
1.9 Multidimensional Linear Interpolation . . . . . . . . . . . . . . 35
1.9.1 Tensor Algebra Concepts . . . . . . . . . . . . . . . . . 35
1.9.2 Multidimensional Data Model . . . . . . . . . . . . . . . 36
1.9.3 Multidimensional Interpolation . . . . . . . . . . . . . . 37
1.10 Nonlinear Array Interpolation . . . . . . . . . . . . . . . . . . . 38
1.10.1 MARS based interpolation . . . . . . . . . . . . . . . . 39
1.10.2 GRNN based interpolation . . . . . . . . . . . . . . . . 40
1.11 Numerical Simulation Results . . . . . . . . . . . . . . . . . . . 41
1.11.1 Multidimensional Linear Performance Results . . . . . . 41
1.11.2 Nonlinear Performance Results . . . . . . . . . . . . . . 44
1.12 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
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2 Cooperative MIMO for Wireless Sensor Networks 51
2.1 Overview and Contribution . . . . . . . . . . . . . . . . . . . . 51
2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.3 Wireless Sensor Networks Organization . . . . . . . . . . . . . 54
2.4 Cooperative MIMO . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.5 Energy Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 58
2.5.1 Conventional Techniques. . . . . . . . . . . . . . . . . . 59
2.5.2 Cooperative MIMO . . . . . . . . . . . . . . . . . . . . 61
2.6 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.6.1 E(cid:27)ects of Synchronization Error on Cooperative MIMO 64
2.6.2 Proposed Coarse Synchronization Scheme . . . . . . . . 65
2.6.3 Proposed Fine Synchronization Schemes . . . . . . . . . 66
2.6.4 Synchronization error propagation . . . . . . . . . . . . 70
2.7 Adaptive C-MIMO Clustering . . . . . . . . . . . . . . . . . . 73
2.7.1 Adaptive C-MIMO clustering . . . . . . . . . . . . . . . 74
2.7.2 Numerical Simulations . . . . . . . . . . . . . . . . . . . 78
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3 Array Processing Localization for Vehicular Networks 87
3.1 Overview and Contribution . . . . . . . . . . . . . . . . . . . . 87
3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.3 Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.4 Space-AlternatingGeneralizedExpectationMaximization(SAGE)
Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.5 Scenario Description . . . . . . . . . . . . . . . . . . . . . . . . 92
3.6 Array Processing Localization . . . . . . . . . . . . . . . . . . . 93
3.6.1 Flip-Flop Estimation . . . . . . . . . . . . . . . . . . . 93
3.6.2 Joint Direct Position Estimation . . . . . . . . . . . . . 96
3.6.3 DOA only estimation . . . . . . . . . . . . . . . . . . . 97
3.6.4 Applicability of DOA Estimation for Positioning . . . . 98
3.7 Three Dimensional DOA Based Estimation . . . . . . . . . . . 99
3.7.1 Scenario Description . . . . . . . . . . . . . . . . . . . . 99
3.7.2 De(cid:28)nition of the Attitude Angles . . . . . . . . . . . . . 99
3.7.3 Direction vector computation . . . . . . . . . . . . . . . 100
3.7.4 Position estimation . . . . . . . . . . . . . . . . . . . . 100
3.8 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.8.1 Results for Simulated Data . . . . . . . . . . . . . . . . 101
3.8.2 Results for Real Data . . . . . . . . . . . . . . . . . . . 104
3.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Conclusion 109
Author’s Publications 113
References 117
viii