Optimum and sub-optimum coherent radar detection in compound Gaussian clutter: A data-dependent threshold interpretation

In this paper we propose a possible approach to the problem of optimum and sub-optimum detection of radar targets against a background of coherent, correlated, K-distributed clutter. It is well-known that the optimum strategy to detect a perfectly known signal embedded in correlated Gaussian clutter is given by comparing the whitening-matched filter output to a given threshold, which is predetermined according to the desired probability of false alarm. When the clutter is non-Gaussian the performance of the matched filter degrades, and the optimum detector in such a disturbance may be non-linear. For the non-Gaussian clutter model considered in this paper, the optimum detector is a function of two statistics: a linear statistic (the matched filter output) and a quadratic statistic. In this paper we show that the optimum detection strategy can be seen as the classical matched filter output compared to an adaptive threshold that depends on the data through the quadratic statistic. This interpretation has given us insight into the structure of the optimum detector and has suggested an approach to obtain, analyze and compare sub-optimum detectors that are easily implementable with performance close to optimal.