Adaptive thresholding of non-homogeneity detection for STAP applications

An adaptive thresholding algorithm is presented that can be used in conjunction with the multi-pass generalized inner product (GIP)-based editing method to eliminate non-homogeneities from the training data used for STAP applications, such as adaptive radars. The algorithm exploits a property of the generic structure of the ordered GIP statistic, along with a single user-specified parameter related to the type I error of incorrectly excising target-free training data, to determine adaptively the thresholds for excising target-contaminated training data. The performance of the method is demonstrated using high-fidelity site-specific simulated data, with both ideal and realistic waveforms, as well as measured data from the multi-channel airborne radar measurement (MCARM) experiment.