Space time adaptive processing algorithm for multiple‐input–multiple‐output radar based on Nyström method

Abstract

In this study, a new space-time adaptive processing (STAP) algorithm based on Nystrom (Nystrom-STAP) method is proposed to adaptively suppress the clutter and jammer in multiple-input-multiple-output (MIMO) radar system. The proposed method can reduce the number of training data and computation complexity with less performance loss. In the algorithm, by exploiting the low-rank characteristic with Nystrom extension strategy and column sampling technique, the Nystrom-based covariance estimator is obtained to approximate the clutter subspace with high accuracy, and it only need small clutter homogenous training data support. Then, combining block-diagonal feature of the jammer covariance matrix and the low-rank property of clutter subspace, the STAP filter can be formed by the inversions of low dimension matrices, which has less computation complexity. In order to further improve the convergence performance of the proposed method, with the help of adaptive displaced phase centre array technique, the reduced-rank Nystrom approaches are proposed. Simulation results demonstrate that the proposed methods exhibit better signal-to-interference-plus-noise ratio and probability of detection performance than conventional STAP algorithms with fewer training data and lower computation complexity.