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In general, the space-time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom (DoFs). However, the performance improvement is accompanied by a dramatic increase in computational cost and training sample requirement. As one of the most efficient dimension-reduced STAP methods, the extended factored approach (EFA) transforms the full-dimension STAP problem into several small-scale adaptive processing problems, and therefore alleviates the computational cost and training sample requirement. However, it cannot effectively work in the airborne MIMO radar since sufficient training samples are unavailable. Aiming at the problem, a fast iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is proposed. In this method, the clutter covariance matrix is estimated by the original data and the constructed data. Then, the spatial weight vector in EFA is decomposed into the Kronecker product of two short-weight vectors. The bi-iterative algorithm is exploited to obtain the desired weight vectors. Simulation results demonstrate the effectiveness of our proposed method.

A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar