TY - GEN
T1 - Recursive moving target detection with distributed MIMO radar in clutter with non-homogeneous power
AU - Wang, Pu
AU - Li, Hongbin
AU - Himed, Braham
PY - 2012
Y1 - 2012
N2 - Our previous study addresses moving target detection (MTD) using a distributed multiple-input multiple-output (MIMO) radar in clutter with non-homogeneous power. The developed detector, referred to as the MIMO-GLRT detector, assumes perfect knowledge of the clutter subspace and uses the assumed clutter subspace to construct a projection matrix which is required to compute the test statistic. In this work, we take into account uncertainties on the clutter subspace, i.e., the subspace dimension is not known a priori, and develop a recursive version of the MIMO-GLRT detector by integrating a computationally efficient updating algorithm for the subspace projection matrix from one iteration to another, together with a generalized Akaike Information criterion (GAIC) for the subspace dimension selection. Simulation results with a synthesized dataset and a general clutter dataset are provided to demonstrate the performance degradation of the standard MIMO-GLRT detector when an over-estimated or under-estimated clutter subspace is used, and show that the recursive MIMO-GLRT detector is able to mitigate such degradation by choosing a proper clutter subspace directly from the received signals.
AB - Our previous study addresses moving target detection (MTD) using a distributed multiple-input multiple-output (MIMO) radar in clutter with non-homogeneous power. The developed detector, referred to as the MIMO-GLRT detector, assumes perfect knowledge of the clutter subspace and uses the assumed clutter subspace to construct a projection matrix which is required to compute the test statistic. In this work, we take into account uncertainties on the clutter subspace, i.e., the subspace dimension is not known a priori, and develop a recursive version of the MIMO-GLRT detector by integrating a computationally efficient updating algorithm for the subspace projection matrix from one iteration to another, together with a generalized Akaike Information criterion (GAIC) for the subspace dimension selection. Simulation results with a synthesized dataset and a general clutter dataset are provided to demonstrate the performance degradation of the standard MIMO-GLRT detector when an over-estimated or under-estimated clutter subspace is used, and show that the recursive MIMO-GLRT detector is able to mitigate such degradation by choosing a proper clutter subspace directly from the received signals.
KW - Distributed MIMO radar
KW - generalized likelihood ratio test
KW - model order selection
KW - moving target detection
KW - non-homogeneous clutter
UR - http://www.scopus.com/inward/record.url?scp=84864257516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864257516&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2012.6212193
DO - 10.1109/RADAR.2012.6212193
M3 - Conference contribution
AN - SCOPUS:84864257516
SN - 9781467306584
T3 - IEEE National Radar Conference - Proceedings
SP - 504
EP - 509
BT - 2012 IEEE Radar Conference
T2 - 2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012
Y2 - 7 May 2012 through 11 May 2012
ER -