TY - JOUR
T1 - Parametric GLRT for multichannel adaptive signal detection
AU - Sohn, Kwang June
AU - Li, Hongbin
AU - Himed, Braham
PY - 2007/11
Y1 - 2007/11
N2 - This paper considers the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance. A parametric generalized likelihood ratio test (GLRT) is developed by modeling the disturbance as a multichannel autoregressive (AR) process. Maximum likelihood (ML) parameter estimation underlying the parametric GLRT is examined. It is shown that the ML estimator for the alternative hypothesis is nonlinear and there exists no closed-form expression. To address this issue, an asymptotic ML (AML) estimator is presented, which yields asymptotically optimum parameter estimates at reduced complexity. The performance of the parametric GLRT is studied by considering challenging cases with limited or no training signals for parameter estimation. Such cases (especially when training is unavailable) are of great interest in detecting signals in heterogeneous, fast changing, or dense-target environments, but generally cannot be handled by most existing multichannel detectors which rely more heavily on training at an adequate level. Compared with the recently introduced parametric adaptive matched filter (PAMF) and parametric Rao detectors, the parametric GLRT achieves higher data efficiency, offering improved detection performance in general.
AB - This paper considers the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance. A parametric generalized likelihood ratio test (GLRT) is developed by modeling the disturbance as a multichannel autoregressive (AR) process. Maximum likelihood (ML) parameter estimation underlying the parametric GLRT is examined. It is shown that the ML estimator for the alternative hypothesis is nonlinear and there exists no closed-form expression. To address this issue, an asymptotic ML (AML) estimator is presented, which yields asymptotically optimum parameter estimates at reduced complexity. The performance of the parametric GLRT is studied by considering challenging cases with limited or no training signals for parameter estimation. Such cases (especially when training is unavailable) are of great interest in detecting signals in heterogeneous, fast changing, or dense-target environments, but generally cannot be handled by most existing multichannel detectors which rely more heavily on training at an adequate level. Compared with the recently introduced parametric adaptive matched filter (PAMF) and parametric Rao detectors, the parametric GLRT achieves higher data efficiency, offering improved detection performance in general.
KW - Generalized likelihood ratio test (GLRT)
KW - Maximum likelihood (ML) parameter estimation
KW - Multichannel signal detection
KW - Parametric models
KW - Space-time adaptive processing (STAP)
UR - http://www.scopus.com/inward/record.url?scp=36248937048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36248937048&partnerID=8YFLogxK
U2 - 10.1109/TSP.2007.896068
DO - 10.1109/TSP.2007.896068
M3 - Article
AN - SCOPUS:36248937048
SN - 1053-587X
VL - 55
SP - 5351
EP - 5360
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 11
ER -