Improved Decentralized SO-CFAR and GO-CFAR Detectors via Moth Flame Algorithm

Optimization of distributed constant false alarm rate (CFAR) system parameters is an essential part in radar detection applications. In this work, the moth flame algorithm (MFO) is proposed as an optimization tool to compute scale factors of distributed Greatest of-CFAR (GO- CFAR) and Smallest of-CFAR (SO-CFAR) detectors in presence of Gaussian disturbance. Local binary decisions are obtained firstly from different sensors, at the fusion center, a fusing rule is applied to obtain a global decision. Detection performances comparisons are conducted against previous works using Gray Wolf Optimization (GWO) and Biography Based Optimization (BBO) methods. Simulation results show that the proposed optimizer demonstrates a slight superiority in some cases for ensuring fixed probability of false alarm and higher detection probabilities.