Adaptive radar detection based on multiple a-priori models

This paper deals with the problem of adaptive radar detection when a limited number of training data, due to environmental heterogeneity, is present. We suppose that some a-priori spectral models for the interference in the cell under test are available and determine Generalized Likelihood Ratio Test (GLRT) based detection algorithms. The basic idea is to model the actual interference inverse covariance as a combination of the available a-priori models and to account for this special structure when a receiver is synthesized. At the analysis stage, we show the capabilities of the new techniques to detect targets when a few training data are available as well as their superiority with respect to conventional adaptive techniques based on the sample covariance matrix.