To solve the problem of large training samples requirement of space time adaptive processing (STAP), a jointly sparse matrices recovery-based method is proposed for clutter plus noise covariance matrix estimation by exploiting the transmitting waveform orthogonality of multiple-in multiple-out (MIMO) radar. The clutter spatio-temporal spectrum estimation problem is modeled as a sparse matrix reconstruction problem. Multiple training samples are generated based on the received signal of MIMO radar from a single training range cell. In order to recover the spatio-temporal spectrum matrices corresponding to different training samples, a jointly 2-D iterative adaptive approach is formulated to provide high accuracy and efficiency. Compared with the existing sparse recovery (SR)-based STAP method with single training range cell, due to the application of matricization process and MIMO radar, the proposed method can not only improve the clutter covariance matrix estimation accuracy but also reduce the computational complexity. In the heterogeneous and non-stationary environment, the proposed method can achieve better clutter suppression performance than conventional SR-based STAP methods using multiple training range cells. Simulation results are given to demonstrate the advantages of the proposed method over existing methods.
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