Data fusion of multiradar system by using genetic algorithm

Detection system with distributed sensors and data fusion are increasingly being used by surveillance systems. There has been a great deal of theoretical study on decentralized detection networks composed of identical or non-identical sensors. To solve the resulting nonlinear system, exhaustive search and some crude approximations are adopted. However, those methods often cause either the system to be insensitive to some parameters or the suboptimal results. In this paper, a novel flexible genetic algorithm is investigated to obtain the optimal results on constant false alarm rate (CFAR) data fusion. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. The simulation results show that adopting the proposed approach, one can achieve better performances than the reported methods and results.