Pre-Compensation Clutter Range-Dependence STAP Algorithm for Forward-Looking Airborne Radar Utilizing Knowledge-Aided Subspace Projection

Teng LONG  Yongxu LIU  Xiaopeng YANG  

Publication
IEICE TRANSACTIONS on Communications   Vol.E95-B   No.1   pp.97-105
Publication Date: 2012/01/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E95.B.97
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2010)
Category: Radars
Keyword: 
space-time adaptive processing (STAP),  clutter spectrum compensation,  subspace projection,  forward-looking airborne radar,  range dependence,  

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Summary: 
The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.