A Robust STAP Method Based on Alternating Projection and Clutter Cancellation

Abstract

Space-time steering vector mismatch of targets usually happens in the space-time adaptive processing (STAP) technique, causing space-time beam distortion. Due to the effects of clutter spectrum broadening and jamming, performance of the conventional STAP method deteriorates dramatically. This paper proposes a novel robust STAP method to improve the performance of clutter suppression and moving target detection. The proposed method employs the alternating projection and clutter cancellation method, to reconstruct the clutter plus jamming plus noise covariance matrix (CJNCM) and re-estimate the target steering vector. Firstly, reconstruction of CJNCM consists of summing the eigenvectors of the projection operator, combining the sample covariance matrix and the integral covariance matrix (ICM) of specific region. Secondly, the re-estimated target steering vector is obtained by estimating the dominant eigenvector of the ICM of the Region of Interest (ROI), which contains the target signal purely because CJNCM is cancelled before estimation. Finally, the robust space-time adaptive filtering weight vector is calculated through the MVDR method with the reconstructed CJNCM and the re-estimated target steering vector. Simulation results indicate that the proposed algorithm shows robust performance against space-time steering vector mismatch, better output signal-to-noise ratio performance and better moving target detection performance than the traditional robust STAP method. INDEX TERMS Space-time adaptive processing, space-time steering vector mismatch, covariance matrix reconstruction, alternating projection, clutter cancellation.

A Novel Fast Iterative STAP Method with a Coprime Sampling Structure

“…In airborne radar systems, space-time adaptive processing (STAP) is crucial for clutter suppression and moving target detection [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. However, the performance of the STAP algorithm is largely affected by the accuracy of clutter covariance matrix (CCM) estimation.…”

Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression

“…However, in actual clutter circumstances, significant heterogeneity emerges because of the inner movement of clutter, topographic variation, and some external factors. As a result, the IID relationship between training samples and CUT is not maintained [10]. This causes the number of IID samples to decrease rapidly, and the error of CCM estimation to increase.…”