A hybrid STAP approach for radar target detection in heterogeneous environments

We address the problem of radar target detection under clutter heterogeneity. Traditional approaches, or two-data set (TDS) algorithms, require a training data set in order to estimate the interference covariance matrix and implement the adaptive filter. When the training data exhibits statistical heterogeneity with respect to the test data, the TDS detectors suffer from a degradation in their performance. 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 propose a novel hybrid approach that combines the SDS and TDS algorithms, taking the degree of heterogeneity into account. We derive the hybrid detectors and propose the use of the generalised inner product as a heterogeneity measure. We also give expressions for their probabilities of false alarm and detection under heterogeneous assumptions. Simulation results show that new detectors combine the advantages of both the TDS and SDS algorithms resulting in improved performance in homogeneous interference as well as robustness to heterogeneity.