Distributed fuzzy maximum-censored mean level detector-constant false alarm rate detector based on voting fuzzy fusion rule

In this study, a new distributed fuzzy maximum-censored mean level detector (MX-CMLD) constant false alarm rate (CFAR) detection based on fuzzy space and voting fuzzy fusion rule is presented. In the distributed fuzzy MX-CMLD CFAR detector, each sensor computes the value of the membership function to the false alarm space from the samples of the reference cells and transmits it to the fusion centre. The values are combined according to the voting fuzzy fusion rule and the credibility measure of each sensor to produce a global membership function to the false alarm space in the fusion centre. The simulation results show that the detection performance of the distributed fuzzy MX-CMLD CFAR detector is better than the other fuzzy distributed detectors in homogeneous and non-homogeneous background. Furthermore, the simulation results indicate that the fuzzy algebraic product operator rule gives better performance than the binary AND and the binary OR fusion rules.