Sub-optimum coherent radar detection in a mixture of K-distributed and Gaussian clutter

The author introduces two suboptimum procedures for the coherent detection of a radar target signal, in the presence of a mixture of K-distributed and Gaussian distributed clutter. As a comparison, the optimum Neyman-Pearson and the whitening matched filter strategies to detect coherent pulse trains against the above mentioned disturbance are also presented. The optimum detection scheme is heavy to implement: it involves a numerical integration with respect to the texture variable of the K distribution. It strongly depends on the parameters of the clutter distribution, thus no predetermined threshold can be assigned to achieve a given probability of false alarm if such parameters are unknown. The preferred sub-optimum approach is based on the estimation of the texture variable, which is then used to determine the likelihood ratio. Applying the maximum likelihood estimate the resulting detection strategy is a linear quadratic functional of the observed vector and is clutter distribution free. The performance of the proposed detector is close to optimal and much better than the whitening matched filter detector; moreover, it guarantees approximately constant false alarm rate behaviour, regardless of the clutter distribution