Decentralized fuzzy CFAR detectors in homogeneous Pearson clutter background

In this paper, we analyze the decentralized CA-CFAR, GO-CFAR and SO-CFAR detectors using fuzzy fusion rules in heavy tailed homogeneous 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 center. For each detector considered, we derive the membership function which maps the decision to the false alarm space and compute the threshold at the fusion center. 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, no improvement is obtained beyond L=11 and GSNR >30dB. In most decentralized fuzzy CFAR detectors considered, the distributed fuzzy SO-CFAR detectors with the 'algebraic sum' fuzzy fusion rule presents the highest probability of detection.

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