TY - JOUR
T1 - Conjugate gradient parametric detection of multichannel signals
AU - Jiang, Chaoshu
AU - Li, Hongbin
AU - Rangaswamy, Muralidhar
PY - 2012/4
Y1 - 2012/4
N2 - The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is reexamined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR) parametric model for the disturbance signal in STAP detection. While the parametric approach brings in benefits such as reduced training and computational requirements as compared with fully adaptive STAP detectors, the PAMF detector as a reduced-dimensional solution remains unclear. This paper employs the conjugate-gradient (CG) algorithm to solve the linear prediction problem arising in the PAMF detector. It is shown that CG yields not only a new computationally efficient implementation of the PAMF detector, a new and efficient AR model order selection method that can naturally be integrated with CG iterations, but it also offers new perspectives of PAMF as a reduced-rank subspace detector. We first consider the integration of the CG algorithm with the matched filter (MF) and parametric matched filter (PMF) when the covariance matrix of the disturbance signal is known. It is then extended to the adaptive case where the covariance matrix is estimated from training data. Important issues such as computational complexity and convergence rate are discussed. Performance of the proposed CG-PAMF detector is examined by using the KASSPER and other computer generated data.
AB - The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is reexamined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR) parametric model for the disturbance signal in STAP detection. While the parametric approach brings in benefits such as reduced training and computational requirements as compared with fully adaptive STAP detectors, the PAMF detector as a reduced-dimensional solution remains unclear. This paper employs the conjugate-gradient (CG) algorithm to solve the linear prediction problem arising in the PAMF detector. It is shown that CG yields not only a new computationally efficient implementation of the PAMF detector, a new and efficient AR model order selection method that can naturally be integrated with CG iterations, but it also offers new perspectives of PAMF as a reduced-rank subspace detector. We first consider the integration of the CG algorithm with the matched filter (MF) and parametric matched filter (PMF) when the covariance matrix of the disturbance signal is known. It is then extended to the adaptive case where the covariance matrix is estimated from training data. Important issues such as computational complexity and convergence rate are discussed. Performance of the proposed CG-PAMF detector is examined by using the KASSPER and other computer generated data.
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U2 - 10.1109/TAES.2012.6178076
DO - 10.1109/TAES.2012.6178076
M3 - Article
AN - SCOPUS:84859842591
SN - 0018-9251
VL - 48
SP - 1521
EP - 1536
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 2
M1 - 6178076
ER -