A novel method for CFAR data fusion

Detection systems with distributed sensors and data fusion are increasingly being used by surveillance systems. There has been a great deal of theoretical study into decentralized detection networks that are composed of similar independent sensors. To solve the resulting nonlinear system, an exhaustive search and some approximation methods are usually adopted. However, these often either cause the system to be insensitive to some parameters or they lead to suboptimal results. In this paper, a genetic algorithm is investigated in order to obtain optimal results on constant false alarm rate (CFAR) data fusion.