TY - GEN
T1 - Bayesian parametric GLRT for knowledge-aided space-time adaptive processing
AU - Wang, Pu
AU - Li, Hongbin
AU - Himed, Braham
PY - 2011
Y1 - 2011
N2 - In this paper, the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance is considered. By modeling the disturbance as a multichannel auto-regressive (AR) model and treating the spatial covariance matrix as a random matrix, a parametric generalized likelihood ratio test (P-GLRT) is developed based on a Bayesian framework. The resulting P-GLRT, which is denoted as the knowledge-aided P-GLRT (KA-PGLRT), employs a fully Bayesian principle and performs a jointly spatio-subtemporal whitening process. The KA-PGLRT detector is able to utilize some prior knowledge through a colored loading step between the prior spatial covariance matrix and the conventional estimate of the P-GLRT. Simulation results verify that the KA-PGLRT detector yields better detection performance over other parametric detectors.
AB - In this paper, the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance is considered. By modeling the disturbance as a multichannel auto-regressive (AR) model and treating the spatial covariance matrix as a random matrix, a parametric generalized likelihood ratio test (P-GLRT) is developed based on a Bayesian framework. The resulting P-GLRT, which is denoted as the knowledge-aided P-GLRT (KA-PGLRT), employs a fully Bayesian principle and performs a jointly spatio-subtemporal whitening process. The KA-PGLRT detector is able to utilize some prior knowledge through a colored loading step between the prior spatial covariance matrix and the conventional estimate of the P-GLRT. Simulation results verify that the KA-PGLRT detector yields better detection performance over other parametric detectors.
KW - Bayesian inference
KW - Knowledge-aided space-time adaptive signal processing
KW - generalized likelihood ratio test
KW - multichannel auto-regressive model
KW - parametric approach
UR - http://www.scopus.com/inward/record.url?scp=80052443195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052443195&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2011.5960553
DO - 10.1109/RADAR.2011.5960553
M3 - Conference contribution
AN - SCOPUS:80052443195
SN - 9781424489022
T3 - IEEE National Radar Conference - Proceedings
SP - 329
EP - 332
BT - RadarCon'11 - In the Eye of the Storm
T2 - 2011 IEEE Radar Conference: In the Eye of the Storm, RadarCon'11
Y2 - 23 May 2011 through 27 May 2011
ER -