Hybrid Detection Approach for STAP in Heterogeneous Clutter

We address the problem of radar target detection under clutter heterogeneity. Traditional approaches, designated as the two-data set (TDS) algorithms, require a training data set in order to estimate the interference covariance matrix and implement the adaptive filter. This training data is usually drawn from range gates adjacent to the cell under test (CUT) that are deemed to be statistically homogeneous with it. When the training data exhibits statistical heterogeneity with respect to the test data, the performance of the TDS detectors degrades. The single-data set (SDS) detectors have been proposed to deal with this problem by operating solely on the test data. In this paper, we present a general hybrid approach that combines the SDS and TDS algorithms, taking the degree of heterogeneity into account. This makes the SDS and TDS detectors special cases of the more general hybrid formulation. We derive the hybrid detectors and propose the use of the generalised inner product as a heterogeneity measure. We analyse the new hybrid detectors and give expressions for the probabilities of false alarm and detection when the clutter is assumed homogeneous, and we assess their performance under heterogeneity using Monte Carlo simulations. The results show that the new detectors outperform both the TDS and SDS algorithms under both homogeneous and heterogeneous interference.