Robust adaptive signal processing methods for heterogeneous radar clutter scenarios
Signal Processing - Special section: New trends and findings in antenna array processing for radar
Fast iterative subspace algorithms for airborne STAP radar
EURASIP Journal on Applied Signal Processing
Spatio-temporal adaptive detector in non-homogeneous and low-rank clutter
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Signal Processing
Subspace Expansion and the Equivalence of Conjugate Direction and Multistage Wiener Filters
IEEE Transactions on Signal Processing - Part II
Operator approach to performance analysis of root-MUSIC androot-min-norm
IEEE Transactions on Signal Processing
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
A representation theorem and its applications to spherically-invariant random processes
IEEE Transactions on Information Theory
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Reducing the number of secondary data used to estimate the Covariance Matrix (CM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. Within this framework, the Low-Rank (LR) structure of the clutter is well-known and the corresponding LR STAP filters have been shown to exhibit a smaller Signal Interference plus Noise Ratio (SINR) loss than classical STAP filters, only 2r secondary data (where r is the clutter rank) instead of 2m (where m is the data size) are required to reach the classical 3dB SNR loss. By using other features of the radar system, other properties of the CM can be exploited to further reduce the number of secondary data; this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval, which results in a persymmetric structure of the noise CM. In this context, we propose to combine this property of the CM and the LR structure of the clutter to perform CM estimation. In this paper, the resulting STAP filter is shown, both theoretically and experimentally, to exhibit good performance with fewer secondary data; 3dB SINR Loss is achieved with only r secondary data.