STAP Training Samples Selection Based on GIP and Volume Cross Correlation

Space-time adaptive processing (STAP) requires reliable training samples to support clutter covariance matrix (CCM) calculation of the cell under test (CUT) in the process of moving target detection. However, the heterogeneous clutter and the existence of outliers in the training samples violate the assumption of independent identically distributed (i.i.d.) with the CUT, which is crucial for statistical estimation. To solve this problem, this letter suggests a new strategy to select training samples that are homogeneous with the CUT. First, the generalized inner product (GIP) method is used to filter training samples with outliers and generate the corresponding load matrix. Second, a distance metric between the training sample clutter subspace and the CUT clutter subspace is formed by the volume cross correlation (VCC) function, and the distance metric is used to judge the clutter similarity between the training sample and the CUT. Finally, the training samples with similar clutter distribution characteristics to the CUT are selected. Experimental results show that STAP performance can be improved effectively, especially in heterogeneous environments.