Performance Analysis of Distributed Fuzzy GO-CFAR Detector in Pearson-Distributed Reverberation

This paper deals with the distributed Constant false alarm rate (CFAR) detection system in heavy tailed reverberation modeled by Pearson distribution. A kind of distributed fuzzy greatest of (FGO) CFAR detection scheme is studied here. The detection system consists of multi-sensors and a fusion center. Each sensor computes the value of membership function, and send it to the fusion center. Fusion center will combine the values to produce a global value according to fusion rules, and compare it to a threshold to decide whether a target is present or not. We consider four fusion rules, max, min, algebraic sum and algebraic product. From the Monte-Carlo simulations, the results indicate that the detection performance of distributed FGO-CFAR detectors based on max fusion rule is more robust in homogeneous environment than that detectors based on the other rules. And the probability of detection increases with the number of detectors.