Adaptive signal processing in non-Gaussian noise backgrounds

This paper discusses the problem of adaptive radar target detection in non-Gaussian noise backgrounds which can be modeled as a spherically invariant random process (SIRP). The estimated covariance matrix is used in an adaptive processing method for signal detection. It is shown that the resulting processor is equivalent to a generalized estimator correlator. Performance analysis is presented in terms of probability of detection versus signal-to-noise-ratio (SNR) for the case of a K-distributed SIRP. Performance comparison of our technique with that of the adaptive matched filter (AMF) of [1] is presented. The method developed in this paper affords considerable performance improvement in non-Gaussian noise backgrounds.