Adaptive clutter suppression based on iterative adaptive approach for airborne radar

Direct Data Domain Sparsity-Based STAP Utilizing Subaperture Smoothing Techniques

“…It first discretizes the whole angle-Doppler plane into = , = ( , > 1) grids, where and are the number of angle and Doppler bins, respectively. Then the received data in (1) can be rewritten as [22][23][24][25][26][27][28][29] x = Φ + n,…”

“…For the D3-SR-STAP algorithm in [20][21][22][23][24], it is hard to obtain a stable and good estimation of interference covariance matrix estimate by using only one snapshot. For the NSR-STAP algorithm using multiple snapshots [24][25][26][27][28][29], although it can obtain a good performance by effectively suppressing the interference in homogeneous environments, it will lead to significant performance degradation in presence of discrete interferers only in the CUT. It is worthy of noting that, for the developed algorithm in [22], the exact prior knowledge of the clutter ridge in the angle-Doppler plane is required; however, it is hard to obtain in practice.…”

“…Recently, sparse recovery (SR) methods have been considered for STAP problems, such as sparsity-based STAP algorithms in [20][21][22][23][24][25][26][27][28][29] and L1-regularized STAP filters in [30,31]. The sparsity-based STAP algorithms are highly related to the model-based approach but do not require the knowledge (such as the clutter ridge) to form the GCM and can be applied to arbitrary array geometries and random slow-time samples [21].…”

“…In fact, supposing that no prior knowledge of the interference is available, they discretize the whole angle-Doppler plane into many small grids, exploit the sparsity of the interference spectrum in all discretized angle-Doppler grids, and utilize the sparse recovery algorithms to recover the interference spectrum. Usually, there are two types of sparsity-based STAP in the literature: one type is the D3 STAP based on sparse recovery (D3-SR-STAP) algorithm which uses only the snapshot in the CUT [20][21][22][23][24]; the other is the normal STAP based on sparse recovery (NSR-STAP) that uses multiple snapshots adjacent to the CUT [24][25][26][27][28][29]. Both types exhibit significantly better performance than conventional STAP algorithms in limited training situations.…”

“…Inspired by the SASM techniques used in the spectral estimation [34], the proposed algorithm can reduce the variance of the estimated interference spectrum resulting in improved signal-to-interference-plus-noise ratio (SINR). Moreover, thanks to the rapid convergence of sparsity-based STAP algorithms [25,26,28], the proposed algorithm does not reduce much the number of DOFs to increase the subsnapshots, which will benefit the recovery of the interference spectrum from single subsnapshot. It should be noted that another method, called the generalized forward/backward (F/B) method [35], also employs the SASM techniques and uses multiple training snapshots adjacent to the CUT to generate multiplicative improvement in the snapshots resulting in improved performance in sample limited cases.…”