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Target Localization in Distributed Multiple-Input Multiple-Output Radar: Algorithms and Analysis

Einemo, Martin LU (2014) FMS820 20142
Mathematical Statistics
Abstract (Swedish)
In this thesis, we address the problem of locating a passive target using a
multiple-input multiple-output (MIMO) radar with widely separated antennas
(WSA). Using a WSA geometry, the spatial diversity of the transmit
and receive antennas are utilized to improve the localization accuracy. First,
common numerical algorithms are examined, including the Newton-Raphson
(NR) method with the maximum likelihood (ML) estimator. The analysis is
continued by linearizing the bistatic range measurements, which correspond
to the sum of transmitter-to-target and target-to-receiver distances. From
this, a quadratically constrained quadratic program (QCQP) for target localization
is formulated. The solution of the QCQP is proved in a mean
and... (More)
In this thesis, we address the problem of locating a passive target using a
multiple-input multiple-output (MIMO) radar with widely separated antennas
(WSA). Using a WSA geometry, the spatial diversity of the transmit
and receive antennas are utilized to improve the localization accuracy. First,
common numerical algorithms are examined, including the Newton-Raphson
(NR) method with the maximum likelihood (ML) estimator. The analysis is
continued by linearizing the bistatic range measurements, which correspond
to the sum of transmitter-to-target and target-to-receiver distances. From
this, a quadratically constrained quadratic program (QCQP) for target localization
is formulated. The solution of the QCQP is proved in a mean
and variance analysis to be an unbiased position estimate whose variance
equals the Cramér-Rao lower bound (CRLB). Since the QCQP formulation
is a problem with high computational complexity, a weighted least squares
(WLS) algorithm is developed to realize the QCQP. Further, an algorithm to
solve the target location by using convex optimization with a semi-de_nite
relaxation (SDR) technique is derived. Simulation results are included to
demonstrate the properties and accuracy of the different algorithms. The
simulations especially shows that the WLS algorithm reaches optimal accuracy,
while having a low computational complexity. The weighted least
squares algorithm was submitted in a paper entitled Weighted Least Squares
Algorithm for Source Localization in Distributed MIMO Radar to the scientific
journal Signal Processing for review. (Less)
Please use this url to cite or link to this publication:
author
Einemo, Martin LU
supervisor
organization
course
FMS820 20142
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Multiple-input multiple-output (MIMO) radar, target localization, bistatic range, weighted least squares, semi-definite relaxation, maximum likelihood, Newton-Raphson method.
language
English
id
4856869
date added to LUP
2014-12-04 08:02:47
date last changed
2015-07-24 13:25:41
@misc{4856869,
  abstract     = {{In this thesis, we address the problem of locating a passive target using a
multiple-input multiple-output (MIMO) radar with widely separated antennas
(WSA). Using a WSA geometry, the spatial diversity of the transmit
and receive antennas are utilized to improve the localization accuracy. First,
common numerical algorithms are examined, including the Newton-Raphson
(NR) method with the maximum likelihood (ML) estimator. The analysis is
continued by linearizing the bistatic range measurements, which correspond
to the sum of transmitter-to-target and target-to-receiver distances. From
this, a quadratically constrained quadratic program (QCQP) for target localization
is formulated. The solution of the QCQP is proved in a mean
and variance analysis to be an unbiased position estimate whose variance
equals the Cramér-Rao lower bound (CRLB). Since the QCQP formulation
is a problem with high computational complexity, a weighted least squares
(WLS) algorithm is developed to realize the QCQP. Further, an algorithm to
solve the target location by using convex optimization with a semi-de_nite
relaxation (SDR) technique is derived. Simulation results are included to
demonstrate the properties and accuracy of the different algorithms. The
simulations especially shows that the WLS algorithm reaches optimal accuracy,
while having a low computational complexity. The weighted least
squares algorithm was submitted in a paper entitled Weighted Least Squares
Algorithm for Source Localization in Distributed MIMO Radar to the scientific 
journal Signal Processing for review.}},
  author       = {{Einemo, Martin}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Target Localization in Distributed Multiple-Input Multiple-Output Radar: Algorithms and Analysis}},
  year         = {{2014}},
}