Covariance matrix estimation for CFAR Detection in correlated heavy tailed clutter
Signal Processing - Signal processing with heavy-tailed models
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In this paper, we consider the problem of radar signal detection in the presence of disturbance, which is assumed to be a mixture of coherent K-distributed and Gaussian distributed clutter. Besides, thermal noise, which is always present in the radar receiver, has been considered. The optimum detector is determined by thresholding an appropriate likelihood ratio test. To properly operate in an interference environment of unknown correlation, the optimum detector needs to adaptively estimate from the data the statistical properties of the interferences. Second-order spectral analysis is unable to separately estimate the correlation structure of K-distributed and Gaussian distributed clutter sources. Their separate estimation can be accomplished only in higher order spectrum domain. To reach this goal, an adaptive algorithm based on second- and higher order cumulants is proposed that removes these drawbacks and is able to operate in an environment of unknown correlation structure. The performance of the adaptive processing scheme has been evaluated by means of Monte Carlo simulations