A Low Complexity Space-Time Adaptive Processing with Sparse Constraint based on Conjugate Gradient Techniques

It is known that the knowledge of sparsity of data is valuable to improve the convergence of space-time adaptive processing (STAP) algorithm in airborne radar. Recently, a STAP algorithm was proposed where the sparsity of beam-Doppler pattern is exploited to achieve good performance. However, a matrix inversion operator is involved in this algorithm, which results in high computational burden. In this paper, we present a novel STAP algorithm which is free of matrix inversion. Precisely, the filter weight vector is first expressed by introducing an intermediate vector. The intermediate vector is then derived iteratively by employing CG techniques under some simple assumptions. The filter weight vector can thus be computed by the derived intermediate vector. Therefore, the proposed algorithm avoids the matrix inversion and achieves low computational complexity. Numerical simulation results are provided to illustrate the performance and superiority of the proposed algorithm.