Exploiting persymmetry for low-rank Space Time Adaptive Processing

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 sh...

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Bibliographic Details
Published in:Signal processing 2014-04, Vol.97 (4), p.242-251
Main Authors: Ginolhac, G., Forster, P., Pascal, F., Ovarlez, J.P.
Format: Article
Language:English
Subjects:
Applied sciences
Clutter
Computer Science
Detection, estimation, filtering, equalization, prediction
Engineering Sciences
Estimates
Exact sciences and technology
Information, signal and communications theory
Linear arrays
Low-rank clutter
Noise
Noise levels
Persymmetry
Perturbation analysis
Pulse repetition interval
Radar
Signal and communications theory
Signal and Image processing
Signal processing
Signal, noise
SIRV
STAP
Telecommunications and information theory
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Summary: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. •Modelization of disturbance by a Low-Rank SIRV clutter plus a white Gaussian Noise.•Combination of the persymmetric property of the Radar system and the Low-Rank STAP filter to propose a new Low-Rank STAP filter.•Performance analysis of the SINR Loss by a perturbation analysis.•Validation of the theoretical result in realistic configurations.•Application on real clutter data.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2013.10.026