CAJ | 학술논문

复杂条件下，分布式顺序统计恒虚警率（OS-CFAR）检测系统的参数选择和检测性能分析是一个典型的非线性优化问题，通常采用数值求解和计算机搜索的方法。但在复杂条件下，特别是当传感器数量较多，或采用分布式OS-CFAR这种双门限参数检测方式时，其计算量会异常庞大。提出了一种基于模拟退火的微粒群优化算法，将模拟退火思想引入到具有杂交和高斯变异的粒子群优化算法中，并采用具有递减w算法，保证算法具有较好的全局搜索能力和较好的收敛性。使用这种方法，在进化100代后，在保证精度达到0.000001，可使所有的系统参数同时得到优化。仿真结果表明，同遗传算法比，虽然该方法收敛速度稍慢，但是可避免遗传算法的早熟问题，同时该方法实施简单方便，便于工程应用。
복잡조건하，분포식순서통계항허경솔（OS-CFAR）검측계통적삼수선택화검측성능분석시일개전형적비선성우화문제，통상채용수치구해화계산궤수색적방법。단재복잡조건하，특별시당전감기수량교다，혹채용분포식OS-CFAR저충쌍문한삼수검측방식시，기계산량회이상방대。제출료일충기우모의퇴화적미립군우화산법，장모의퇴화사상인입도구유잡교화고사변이적입자군우화산법중，병채용구유체감w산법，보증산법구유교호적전국수색능력화교호적수렴성。사용저충방법，재진화100대후，재보증정도체도0.000001，가사소유적계통삼수동시득도우화。방진결과표명，동유전산법비，수연해방법수렴속도초만，단시가피면유전산법적조숙문제，동시해방법실시간단방편，편우공정응용。
For a distributed Ordered Statistics(OS)Constant False Alarm Ration(CFAR)detection system, the searching of the optimum detector parameters and detection performance is a typical nonlinear optimization problem. It is very diffi-cult to choose system parameters to obtain optimal threshold values at the fusion center for two threshold parameters detection method like distributed OS-CFAR, especially the number of sensors is huge. This paper provides a novel solution based on an effective and flexible Particle Swarm Optimization(PSO)algorithm. The simulated annealing idea is intro-duced into the PSO algorithm with crossover and mutation of Gauss, and decreasing w algorithm is employed, to ensure the advantage of global searching ability and convergence. Using this method, while the accuracy is 0.000001, with the evolution of 100 generations,it can make all the system parameters optimize. Compared with genetic algorithm, simula-tion results show that, this method can avoid premature problem of genetic algorithm, although this method converges slightly slower. Moreover the method is easy and simple to implement, and it is convenient for engineering applications.