An adaptive CFAR signal detector for spatially correlated noise samples

The authors study the problem of adaptive constant false alarm rate (CFAR) detection in a spatially correlated background environment. The clutter is modeled spatially as a first-order Markov Gaussian process, while the target return is assumed to be Rayleigh envelope distributed. The case where the clutter power is much higher than the thermal noise power is considered. An expression for the probability of false alarm of the CA-CFAR (cell-averaging-CFAR) detector is derived. In the analysis, the covariance matrix of the total noise (thermal noise plus clutter) is approximated by the covariance matrix of the clutter. It is shown that the CFAR parameters of the CA-CFAR detector are dependent on the clutter covariance matrix and that the achieved probability of false alarm may be degraded up to five orders of magnitude when the degree of correlation of the clutter samples is high. In order to alleviate this problem, a generalized CA-CFAR detector that adapts to changes in both the clutter level and the clutter covariance matrix is proposed