localization, tracking, and antenna allocation in multiple-input multiple-output radars

180 Pages·2012·1.48 MB·English

by  Aliakbar Gorji Daronkolaei

LOCALIZATION, TRACKING, AND ANTENNA ALLOCATION IN
MULTIPLE-INPUT MULTIPLE-OUTPUT RADARS
LOCALIZATION, TRACKING, AND ANTENNA ALLOCATION
IN MULTIPLE-INPUT MULTIPLE-OUTPUT RADARS
By
ALIAKBAR GORJI DARONKOLAEI, B.Sc., M.Sc.
A Thesis
Submitted to the School of Graduate Studies
in Partial Fulfillment of the Requirements
for the Degree
Doctor of Philosophy
McMaster University
c Copyright by Aliakbar Gorji Daronkolaei, September 2012
(cid:13)
DOCTOR OF PHILOSOPHY (2012) MCMASTER UNIVERSITY
(Electrical and Computer Engineering) Hamilton, Ontario
TITLE: Localization, Tracking, And Antenna Allocation
in Multiple-input Multiple-output Radars
AUTHOR: Aliakbar Gorji Daronkolaei
B.Sc., M.Sc.
SUPERVISOR: Dr. Thia Kirubarajan
NUMBER OF PAGES: xx, 159
ii
To my lovely parents who were my main supporters
Abstract
This thesis concerns with the localization, tracking, and sensor manage-
ment in the Multiple-Input Multiple-Output (MIMO) radar systems. The col-
located and widely-separated MIMO radars are separately discussed and the
signal models are derived for both structures. The first chapter of the thesis is
dedicated to the tracking and localization in collocated MIMO radars. A novel
signal model is first formulated and the localization algorithm is developed for
the derived signal model to estimate the location of multiple targets falling in
the same resolution cell. Furthermore, a novel tracking algorithm is proposed
in which the maximum bound on the number of uniquely detectable targets
in the same cell is relaxed. The performance of the tracking and localization
algorithms is finally evaluated using the tracking Posterior Cramer-Rao Lower
Bound (PCRLB). After showing the impact of the antennas position on the
localization CRLB, a novel sensor management technique is developed for the
collocated MIMO radars in Chapter 4. A convex optimization technique is
proposed for the antenna allocation in a single-target scenario. When multi-
ple targets fall inside the same cell, a sampling-based technique is formulated
to tackle the non-convexity of the optimization problem.
The third chapter of this thesis also proposes new approaches for detection,
localization, and tracking using a widely-separated MIMO radar. A scenario
with multiple-scatterer targets is considered and the detection performance of
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both MIMO and multistatic radars will be evaluated in the designed scenario.
Toestimatethelocationofthemultiple-scatterertarget,aMultiple-Hypothesis
(MH) based approach is proposed where the number and the location of multi-
ple targets are both estimated. A particle filter based approach is also formu-
lated for the dynamic tracking by a widely-separated MIMO radar. Finally,
the performance of the MIMO radar and the miultistatic radar in detecting
and localizing multiple-scatterer targets is studied.
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Acknowledgements
During writing this thesis I have met and interacted with many people who
have influenced and contributed to the path of my research. I first would like
to express my deepest gratitude to Professor Thia Kirubarajan, who has been
my thesis supervisor during the past four years. He introduced me to the field
of target tracking, radar systems and many interesting ideas in a very relaxed
atmosphere. Defining and writing this thesis would not be certainly possible
without professor Kirubarajan’s supervision and encouragements.
I am also grateful to Dr. Ratnasingham Tharmarasa for contributing enor-
mously towards my understanding of radar systems and tracking algorithms.
A significant part of my thesis contributions is a result of Thamas’ great ideas
and helpful suggestions. I really enjoyed interacting with you, Thamas!
I would like to specially thank Professor Brian Anderson, who was my
supervisor during my visit totheAustralian NationalUniversity (ANU). Brian
hasbeenthemost humble, resourceful, andthegentlest personwho Ihave ever
met in my life. I learnt many things from Brian in both academic and ethical
points of view. I always wish I could have another opportunity to work with
Brian.
Besides mysupervisors, Ihave been alsofortunateto collaboratewithsome
other academic members at McMaster. I am specially indebted to Professor
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Jim Reilly and Dr. Jian-Kang Zhang as the members of my graduate com-
mittee. I also thank Professor Tim Davidson for his constructive and helpful
comments on the optimization and signal processing aspects of MIMO radars.
Many other people have helped me, directly or indirectly, at different
stages of my PhD study. I would like to thank my friends and colleagues
at McMaster including Nima Zarif, Keyvan Hosseinkhani, Reza Haghshenas,
Sina Naderi, Hamed Afshari, Mina Attari, Kasra Asadzadeh, Sadegh Dadash,
Bahram Marami, Ramin Mafi, Mehdi Nabaee, Cheryl Gies, Dr. Nandaku-
maran Nadarajah, Xin Chen, Darcy Dunne, and Biruk Habtemariam. My
special thanks also go to Dr. Brad Yu, Dr. Adrian Bishop, Dr. Mark Reed,
Mohsen Zamani, Mohammad Deghat, Morteza Azad, Alireza Motevallian, and
Elspeth Davis who helped me during my visit at ANU. My whole PhD study
would never be finalized without the help of above people.
Over the last four years, I was supported by two McMaster university
graduate scholarship, and prestigious Sherman Clifford scholarship. I am most
grateful for this financial assistance.
At the end, I want to thank my wife, parents and my brother for all love,
support, and motivation.
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List of Acronyms
2D Two Dimensional
RCS Radar-target Cross-section
MIMO Multipl-input Multiple-output
CRLB Cramer-Rao Lower Bound
FIM Fisher Information Matrix
PCRLB Posterior Cramer-Rao Lower Bound
DOA Direction of Arrival
SDP Semi-definite Programming
MSE Mean Squared Error
MMSE Minimum Mean Squared Error
RMSE Root Mean Squared Error
MHT Multiple-hypothesi Tracking
MH Multiple Hypothesis
TBD Track-Before-Detect
SNR Signal-to-Noise Ratio
KF Kalman Filter
EKF Extended Kalman Filter
UKF Unscented Kalman Filter
GRLT Generalized Likelihood Ratio Test
BLUE Best Linear Unbiased Estimator
ML Maximum Likelihood
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MDL Minimum Distance Length
ROC Receiver Operating Characteristic
TOT Time-On-Target
TOA Time of Arrival