Training Sample Selection for Space-Time Adaptive Processing in Heterogeneous Environments

As training samples are not always identically distributed with the clutter in the cell under test (CUT) in heterogeneous environments, the estimated clutter covariance matrix for space-time adaptive processing (STAP) is not accurate, which degrades the performance of STAP. To improve the performance of STAP in heterogeneous environments, this letter proposes a novel training sample selection algorithm to estimate the covariance matrix. Based on the subaperture smoothing techniques, subapertures´ covariance matrices are estimated, which are used to measure the similarities between the clutter covariance matrix of the CUT and the clutter covariance matrices of the training samples. Training samples whose clutter covariance matrices are similar to that of the CUT are selected, leading to a better estimation of the clutter covariance matrix, and the performance of STAP improves. Experimental results confirm the performance of the proposed algorithm.