Generalised decentralised fuzzy CA-CFAR detector in Pearson distributed clutter

In this paper, we analyze the decentralized CA-CFAR detector using fuzzy fusion rules in heavy tailed clutter modeled by a Pearson distribution. We generalize our study by considering a distributed detection system with "L" detectors and using the "Maximum", "Minimum", "Algebraic sum" and "Algebraic product" fuzzy rules at the data fusion centre. We derive the membership function which maps the decision to the false alarm space and compute the threshold at the fusion centre. From the Monte-Carlo simulations conducted to assess the detection performance in homogeneous Pearson distributed clutter, we observe that the probability of detection increases with the number of detectors. However, there a maximum number of detectors (L = 11) above which no improvement is obtained.