Robust STAP of Dictionary Local Adaptive Filling and Learning for Nonstationary Clutter Suppression

With the vigorous development of sparse recovery (SR) theory, the research results are gradually applied in the field of space-time adaptive processing (STAP), aiming at reducing the dependence of the system on the number of training samples. However, for nonsidelooking airborne radar, it should be noted that the dictionary mismatch caused by off-grid is a major problem related to the SR framework. In addition, we should recognize that there are always problems with array error and intrinsic clutter motion in radar systems, which result in reduced robustness of STAP. In view of the above challenges, this article proposes a clutter space-time amplitude spectrum reconstruction scheme based on local adaptive filling and learning of redundant dictionaries. First, describe the distribution of clutter and noise energy in space-time 2-D spectrum, and then the boundary of clutter distribution region is determined. Next, the clutter ridge of the selected training sample is modeled according to the array configuration and discretized as mesh points filled into an empty dictionary. Then, the free locations within and outside the clutter boundary are further extended according to different mesh densities. Finally, on the premise of retaining the structural features of the original dictionary, we introduced the correction matrix using Hadamard product, and through the Bayesian framework, parameterized iterative updating of effective grid is carried out indirectly, aiming at accurately fitting the weighted vectors of nonideal factors. Experiment results demonstrate the clutter suppression performance under low sample support and the robustness to deal with nonideal factors.