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. A performance comparison of our technique with that of the adaptive matched filter (AMF) of Robey et al (1992) is presented. The method developed in this paper affords considerable performance improvement in non-Gaussian noise backgrounds.