论文部分内容阅读
针对采用K/N融合准则的并行分布式恒虚警检测问题,提出了一种基于区间编码遗传算法的优化方法。选取N?1个局部虚警概率作为优化变量。依据约束条件,采用逐个优化技术依次设计各个局部虚警概率的编码区间,将约束优化转化为无约束优化,克服了遗传算法中通常采用惩罚技术引起的迭代次数增加的缺点。将该优化方法应用于天基SAR多视角协同探测中,确定了每个局部判决器的标称化因子,对系统中包含2个、3个和4个独立SAR系统的情况分别进行了仿真验证,并对比分析了K、N变化时的检测性能。
Aiming at the problem of parallel distributed CFAR detection using K / N fusion criterion, an optimization method based on interval coding genetic algorithm is proposed. Select N 1 local false alarm probability as optimization variables. According to the constraints, the coding interval of each local false alarm probability is designed one by one using the optimization technique one by one, and the constraint optimization is transformed into the unconstrained optimization, which overcomes the shortcoming that the number of iterations caused by the penalty technique in genetic algorithm usually increases. The optimization method is applied to multi-view cooperative detection of space-based SAR, and the nominal factor of each local arbiter is determined. The simulation results of the system including 2, 3 and 4 independent SAR systems are respectively verified , And compared the detection performance when K and N changes.