Direction-of-Arrival estimation of RF signals based on circular arrays

83 Pages·2011·15.43 MB·English

by  Lanseloet Erbuer

Direction-of-Arrival estimation of RF signals based on
virtual circular arrays
Lanseloet Erbuer
Promotor: prof. dr. ir. Hendrik Rogier
Begeleiders: ing. Patrick Van Torre, ir. Pieterjan Demarcke
Masterproef ingediend tot het behalen van de academische graad van
Master in de ingenieurswetenschappen: elektrotechniek
Vakgroep Informatietechnologie
Voorzitter: prof. dr. ir. Dani¨el De Zutter
Faculteit Ingenieurswetenschappen en Architectuur
Academiejaar2010-2011
ii
The author gives the permission to use this thesis for consultation and to copy parts of it
for personal use. Every other use is subject to the copyright laws, more specifically the
source must be extensively specified when using from this thesis.
Ghent, August 2008
The Author
Lanseloet Erbuer
ii
Acknowledgments
Thank you Ghislaine Duerinckx and John Erbuer for raising me and giving me
the freedom to follow my own path.
Thank you ing. Patrick Van Torre for assisting me throughout the year with this
thesis, for showing the necessary patience towards me and for providing me with numerous
helpful tips.
Thank you Prof. dr. ir. Hendrik Rogier for providing me with the subject of
my thesis and the all-important guidance.
Thank you Moenen and Merlijn Erbuer for forgiving me my India catastrophe.
I am terribly sorry.
Thank you Joylene Dumarey for all the incredible fun we have together. I love
you.
iii
Direction-of-Arrival estimation of RF signals based on
virtual circular arrays
by
Lanseloet ERBUER
Scriptie ingediend tot het behalen van de academische graad van
Master in de Ingenieurswetenschappen: Elektrotechniek
Academiejaar 2010-2011
Promotor: Prof. dr. ir. H. Rogier
Begeleiders: ing. P. Van Torre, ir. P. Demarcke
Faculteit Ingenieurswetenschappen en Architectuur
Universiteit Gent
Vakgroep Informatietechnologie
Voorzitter: Prof. dr. ir. D. De Zutter
Abstract
Wireless communication services have known an explosive growth in the last decade. As
the group of wireless users unceasingly expands and with the recent shift in emphasis from
voice towards multimedia applications, research towards Smart Antennas emerged to cope
with the necessary increase in channel throughput. Smart antennas allow Spatial-Division
Multiple Access, which spatially separates signals based on their position, using Direction-
of-Arrivalfindingalgorithms. Inthiswork, usingvirtualcirculararrays, webuildandtesta
number of well known DoA estimation algorithms, such as Bartlett, Capon, MUSIC, Root-
MUSIC and ESPRIT. We compare their performance through simulations and subject the
best performing DoA estimation algorithm to the challenge of estimating the location of
multiple transmitters in an outdoor environment, subject to fading, multipath propagation
and thermal noise.
Keywords
Direction-of-Arrival (DoA) Estimation, Bartlett, Capon, MUSIC, Root-MUSIC, ESPRIT,
Virtual Uniform Circular Array (UCA), Smart Antennas, Model Order Estimation.
iv
Direction-of-arrival estimation of RF signals based on
virtual circular arrays
Lanseloet Erbuer
Supervisor(s): ing. Patrick Van Torre, prof. dr. ir. Hendrik Rogier
Abstract – Wireless communication services have known an explosive growth in the last
decade. To enable the necessary increase in channel throughput, research towards smart
antennas (SAs) emerged. Using direction- of-arrival (DoA) finding algorithms, SAs allow to
spatially separate signals based on their position. In this work, using virtual circular arrays,
we examine a number of well known DoA estimation algorithms, namely Bartlett, Capon,
MUSIC, and ESPRIT. We compare their performance through simulations and subject the
best performing algorithm to the challenge of estimating the location of multiple receivers
in a realistic outdoor environment, subject to fading and multipath propagation.
Keywords – Direction-of-Arrival Estimation, MUSIC, Virtual Uniform Circular Array
I. Introduction niques [1] will estimate the mean power P in
function of the look angle Φ. By determin-
Today, mobile communication technology is
ing the local maxima in the power spectrum
omnipresent. Limited available bandwith and
P(Φ), we find the DoA estimates. A problem
a growing number of mobile subscribers, in-
with these techniques is that they are not al-
troduces major challenges. Over the years,
ways sufficiently accurate, as their resolution
FDMA, TDMA and CDMA were the perfect
depends on the search step. Model-based DoA
answer to the high demands. As the group
estimation [1] computes the DoAs directly and
of wireless users unceasingly expands, research
is thus search-free. Drawbacks of this proce-
towards smart antennas (SAs) emerged, to at-
dure are added complexity and a higher com-
tainimprovementsinthesystemcapacity. SAs
putational load.
allow the application of Space-Division Mul-
tiple Access, which spatially separates signals
– i.e. based on their location. To determine
the positions of the users, Direction-of-Arrival
(DoA) finding algorithms are used. The aim of
thisworkistoexamineanumberofwellknown
DoAestimationalgorithmsthatusevirtualcir-
culararraysasarrayconfiguration. Wesubject
the best performing DoA finding algorithm to
the challenge of estimating both azimuth and
elevationangleofmultipletransmittersinare-
Figure 1: Mean estimation error vs. SNR.
alistic outdoor environment.
We compare the mean estimation error of
Bartlett, Capon, MUSIC (spectral-based) and
UCA-ESPRIT(model-based)forvaryingSNR.
II. Implementation
Result are shown in Figure 1. For relatively
high SNR, MUSIC and UCA-ESPRIT are pre-
A. DoA finding Algorithms
ferred. ESPRIT is not considered further due
We can subdivide the DoA estimation meth- to practical implications of the measurement
ods into spectral-based and model-based DoA setup that limit its efficacy. We thus concen-
methods. Spectral-basedDoAestimationtech- trate on the use of MUSIC.
v
B. Measurement Setup
The main interest of this work lies in DoA es-
timation using virtual uniform circular arrays
[2]. These, compared to linear arrays, offer
the important advantages that they are able
to scan (1) the full azimuthal range and (2)
a (restricted) elevation range. Disadvantage is
thatcirculararrayshaveahighersidelobelevel
than that of linear arrays.
Figure 3: MUSIC spectrum with LoS.
B. Results
Next we conduct the experiment as depicted
in Figure 2. The results, after applying spatial
smoothing (SS) and a Chebyshev window, are
shown in Figure 4.
Figure 2: Test environment.
In our experiment (see Figure 2), after exten-
sive testing of the algorithms, we estimate the
DoAs of two transmitters, present in an alley
and in absence of a line-of-sight (LoS) between
them and the receiver. We add complexity
to the situation by placing a car in the alley,
Figure 4: MUSIC spectrum non LoS.
which generates a lot of extra reflections.
We only measured with a horizontally placed
UCA, this causes elevation ambiguity. We see
that MUSIC looses a big part of its resolution,
III. Results
mainly caused by SS and the Chebyshev win-
dow. In the spectrum, we clearly see (1) the
sources – the signal diffracts around the cor-
A. Validation
ner – and (2) the car who reflects the signals.
First we validate our algorithms by introduc-
Further works should concentrate on ESPRIT,
ing a LoS between transmitters and receiver,
which provides both higher accuracy and reso-
in the alley shown in Figure 2. The car is
lution compared to spectral-based methods.
not yet in place. The resulting MUSIC power
spectrum is shown in Figure 3. The crosses
denote the real DoAs, the peaks in the contour
plot the estimations. We obtain this result References
by multiplying the spectra from two separate
measurements, where we first horizontally and [1]Z.Chen,G.Gokeda,andY.Yu. Introduction to
then vertically align the UCA. This removes Direction-of-ArrivalEstimation ArtechHousePub-
theambiguitiesfromtheestimation. Themean lishers, 2010.
estimation errors are 3.3◦ and 8.2◦ for azimuth [2] H. Rogier. Antennas and Propagation Univer-
and elevation angle resp.
sity of Ghent, 2008.
Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Structure of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Methods of Direction-of-Arrival Estimation 4
2.1 The Direction-of-Arrival Estimation Problem . . . . . . . . . . . . . . . . . 4
2.2 Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Spectral-Based DoA Estimation Techniques . . . . . . . . . . . . . . . . . 8
2.3.1 Bartlett . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.2 Capon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.3 Subspace Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.4 Spectral-MUSIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.5 Resolution Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Model-Based DoA Estimation Techniques . . . . . . . . . . . . . . . . . . 16
2.4.1 Root-MUSIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 ESPRIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.1 Estimation of Number of Sources . . . . . . . . . . . . . . . . . . . 20
2.5.2 Decorrelating Coherent Signals . . . . . . . . . . . . . . . . . . . . 23
2.6 Matlab Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Measurement Setup 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Measuring Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.1 Circular Antenna Arrays . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.2 Virtual Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.3 Anechoic Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.4 Signalion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3 Anechoic Chamber Experiment . . . . . . . . . . . . . . . . . . . . . . . . 35
vi
Contents vii
3.4 Outdoor Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4.1 Direct Communication Link . . . . . . . . . . . . . . . . . . . . . . 40
3.4.2 No Direct Communication Link . . . . . . . . . . . . . . . . . . . . 42
3.4.3 No Direct Communication Link and Increased number of Reflections 42
4 Results and Interpretation 44
4.1 Anechoic Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.1 Virtual UCA with 90 elements . . . . . . . . . . . . . . . . . . . . . 44
4.2 Outdoor Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.1 Virtual UCA with 90 elements . . . . . . . . . . . . . . . . . . . . . 57
5 Summary 65
5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Bibliography 67
List of Figures 69
List of Tables 73
Chapter 1
Introduction
1.1 Motivation
In the last decade, wireless communication services have known an explosive growth. Ac-
cording to the International Telecommunication Union (ITU)(1), in the past five years
the number of mobile cellular subscriptions worldwide doubled, from 2.2 billion in 2005
to a tremendous 5.3 billion in 2010. 90 percent of the world population now has access
to mobile networks. There is a constant demand for cheaper and more efficient portable
devices. To sustain this enormous expansion of the mobile telecommunication market, the
modern mobile technology has to evolve in like manner.
Figure 1.1: Number of the mobile cellular subscriptions from 2005 to 2010, according to the
ITU (1)
1