TY - GEN
T1 - Conjugate gradient parametric adaptive matched filter
AU - Jiang, Chaoshu
AU - Li, Hongbin
AU - Rangaswamy, Muralidhar
PY - 2010
Y1 - 2010
N2 - The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is re-examined 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 significantly 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, but it also offers new perspectives of PAMF as a reduced-rank subspace detector. The CG algorithm is first introduced to provide alternative implementations for 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 unknown model order 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 re-examined 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 significantly 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, but it also offers new perspectives of PAMF as a reduced-rank subspace detector. The CG algorithm is first introduced to provide alternative implementations for 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 unknown model order 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/RADAR.2010.5494524
DO - 10.1109/RADAR.2010.5494524
M3 - Conference contribution
AN - SCOPUS:77954918725
SN - 9781424458127
T3 - IEEE National Radar Conference - Proceedings
SP - 740
EP - 745
BT - 2010 IEEE Radar Conference
T2 - IEEE International Radar Conference 2010, RADAR 2010
Y2 - 10 May 2010 through 14 May 2010
ER -