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.
"A Novel Non-Homogeneous STAP Algorithm for Target-Like Signal Elimination Based on Sparse Reconstruction," Progress In Electromagnetics Research M,
Vol. 72, 153-163, 2018. doi:10.2528/PIERM18051006
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