Optimization Of Radar Cfar Detection Algorithm And Threshold Decision Research

The purpose of CFAR detection technology in radar signal processing is to distinguish the target echo from the clutter. This technology impacts the whole performance of radar system. In order to guarantee the probability of detection higher than a certain level, the constant false alarm rate method is applied in radar signal processing. First of all, the threshold which can avoid the influence of the background clutter and noise must be set so that the false alarm rate of the detector is constant when detecting the target.There are different degrees of limitations in the performance of the traditional CFAR algorithm. The processes to obtain the threshold are relatively complicated for the common CFAR algorithm. As two typical intelligent optimization methods, Genetic algorithm (GA) and Particle Swarm Optimization (PSO) can improve the process and precision when solving the threshold.In this paper, GA and PSO which applied in the optimization of solving the threshold of CFAR detector are studied. The main details include the following several aspects:The basic concepts, principles and the process of the CFAR algorithm are introduced. The CA-CFAR which is covered in the class of the ML CFAR is detailed studied. The performance of CA-CFAR detector under two modes (linear and square-law) of detection is compared. The threshold under different false alarm rate and different number of range cells are studied by computer simulation. The detection performance under the influence of these factors is also researched.Several other algorithms of the class of ML CFAR are studied and the ADT of these algorithms are compared. The performance of these algorithms in homogeneous environments, multi-target interference environments and clutter edge environments are analyzed.The basic concepts and principles of GA and PSO are introduced. Their operation steps are detailed analyzed. The similarities and differences between GA and PSO are compared. Optimize the algorithm of the threshold with GA and make the optimization for the algorithm of threshold factors with PSO. The simulation shows that the results by PSO are better and superior than GA.