Space-time adaptive processing (STAP) for airborne radar employs training samples to estimate clutter covariance matrix (CCM). However, the target-like signals contained in the training samples severely corrupt the accuracy of the CCM. This paper proposes a novel non-homogeneous STAP algorithm for target-like signal elimination based on reduced-dimension sparse reconstruction (RDSR) to overcome this issue. The proposed algorithm exploits the high-resolution angle-Doppler spectrum obtained by RDSR to estimate and eliminate target-like signals. Theoretical analysis and simulation results show that the proposed algorithm effectively suppresses clutter and improves the performance of STAP in non-homogeneous environments.
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