Adaptive ML-CFAR detection for correlated chi-square targets of all fluctuation models in correlated clutter and multiple target situations

The problem of adaptive CFAR detection of a pulse-to-pulse partially correlated target with 2 K degrees of freedom in a pulse-to-pulse partially Rayleigh correlated clutter and multiple target situations is addressed. The target and the clutter covariance matrices are modeled as first-order Markov processes. The probability of detection for the mean level (ML) detector is shown to be sensitive to the degree of correlation of the target returns and the degree of correlation of the clutter returns as well.