Abstract: In heterogeneous clutter environment, the training samples contaminated by outliers seriously degrade the performance of space-time adaptive processing (STAP) and needs to be eliminated. Therefore, this paper proposes an output signal-to-clutter-plus-noise ratio (SCNR) based training sample selection algorithm. The output SCNR is acted as the test statistic for training samples selection. When the clutter property of the training sample is more similar to the cell under test (CUT), the clutter suppression performance is better, and the output SCNR is higher. Moreover, this paper exploits the subaperture’s covariance matrix to directly characterize the clutter property of each range cell, ensuring the characterization is not affected by others, and the estimation accuracy does not depend on the number of the samples. Finally, the numerical experiments with measured data demonstrate the performance of the proposed training samples selection method.