Optimum second threshold for the CFAR binary integrator in Pearson-distributed clutter

In this paper, we propose to analyze the binary integration of the cell-averaging constant false-alarm rate (CA-CFAR) and order statistics constant false-alarm rate (OS-CFAR) detectors in the presence of non-Gaussian spiky clutter modeled as a Pearson distribution. We derive new closed form expressions for false alarm and detection probabilities for the CA-CFAR detector in the presence of Pearson-distributed clutter backgrounds. We first show that the use of binary integration improves the detection probabilities of the detectors considered. Secondly, the maximum of detection probability occurs for an optimum choice when the second threshold is set to be equal to M = (3/4) L. For this optimum M-out-of-L rule, the comparison analysis of the CA-CFAR and OS-CFAR binary integrators showed that the latter has better performance in homogeneous Pearson- distributed clutter.

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