Combined Membership Functions in Fuzzy Signal Detection

In this paper, we propose a model for a constant false alarm detection (CFAR) that uses fuzzy logic to describe the uncertainty on the decision about the presence or the absence of a target. The received signal is processed and the membership function to three regions, namely; "signal present", "signal absent" and "uncertainty" is evaluated. The membership functions from different observations are combined according to some fuzzy fusion rules to obtain the global decision. The results obtained showed that the fusion rule defined by min(x+y, 1) gives better performance detection than the other rules