Adaptive CFAR detection in partially correlated clutter

The problem of adaptive constant false alarm rate (CFAR) detection in a spatially correlated background environment is studied. The clutter is modelled spatially as a first-order Markov Gaussian process, whilst 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 and an expression is derived for the actual probability of false alarm of the (cell-averaging) CA-CFAR detector. 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 sample is high. To alleviate this problem, the authors propose a generalised CA-CFAR (GCA-CFAR) detector that adapts not only to changes in the clutter level but also to changes in the clutter covariance matrix