TY - GEN
T1 - Bayesian parametric approach for multichannel adaptive signal detection
AU - Wang, Pu
AU - Li, Hongbin
AU - Himed, Braham
PY - 2010
Y1 - 2010
N2 - This paper considers the problem of space-time adaptive processing (STAP) in non-homogeneous environments, where the disturbance covariance matrices of the training and test signals are assumed random and different with each other. A Bayesian detection statistic is proposed by incorporating the randomness of the disturbance covariance matrices, utilizing a priori knowledge, and exploring the inherent Block-Toeplitz structure of the spatial-temporal covariance matrix. Specifically, the Block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process and hence, develop the Bayesian parametric adaptive matched filter (B-PAMF) to mitigate the training requirement and alleviate the computational complexity. Simulation using both simulated multichannel AR data and the challenging KASSPER data validates the effectiveness of the B-PAMF in non-homogeneous environments.
AB - This paper considers the problem of space-time adaptive processing (STAP) in non-homogeneous environments, where the disturbance covariance matrices of the training and test signals are assumed random and different with each other. A Bayesian detection statistic is proposed by incorporating the randomness of the disturbance covariance matrices, utilizing a priori knowledge, and exploring the inherent Block-Toeplitz structure of the spatial-temporal covariance matrix. Specifically, the Block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process and hence, develop the Bayesian parametric adaptive matched filter (B-PAMF) to mitigate the training requirement and alleviate the computational complexity. Simulation using both simulated multichannel AR data and the challenging KASSPER data validates the effectiveness of the B-PAMF in non-homogeneous environments.
KW - Bayesian detection
KW - Non-homogeneous environments
KW - Parametric adaptive matched filter
KW - Space-time adaptive signal processing
UR - http://www.scopus.com/inward/record.url?scp=77954901060&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954901060&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2010.5494503
DO - 10.1109/RADAR.2010.5494503
M3 - Conference contribution
AN - SCOPUS:77954901060
SN - 9781424458127
T3 - IEEE National Radar Conference - Proceedings
SP - 838
EP - 841
BT - 2010 IEEE Radar Conference
T2 - IEEE International Radar Conference 2010, RADAR 2010
Y2 - 10 May 2010 through 14 May 2010
ER -