改进麻雀算法在天波雷达定位中的应用

Application of Improved Sparrow Algorithm in Sky-Wave Radar Location

针对复杂战场环境要求天波雷达快速精准定位的问题,提出一种基于多策略改进麻雀搜索算法的定位模型。首先,使用立方混沌映射、步长因子动态调整、反向学习和混合变异算子对麻雀搜索算法进行改进,形成改进的麻雀搜索算法;然后,采用改进后的麻雀搜索算法寻找混合核极限学习机(HKELM)最优的核函数参数和混合核的权重系数;最后,使用寻优后的HKELM对天波雷达探测到的目标进行定位。结果表明,改进后的麻雀搜索算法在精度上和稳定性上优于用基本麻雀搜索算法改进的HKELM模型和极限学习机(ELM)定位模型,表明了所提算法的有效性。

As complex battlefield environment requires rapid and accurate positioning of skywave radar,a positioning model based on multistrategy improved sparrow search algorithm is proposed.First,cubic chaotic mapping,dynamic adjustment of step factor,reverse learning,and mixed mutation operator are used to invent an improved sparrow search algorithm.Then,the improved sparrow search algorithm is used to find the bestfit kernel function parameters and weight coefficient of mixed kernel of hybrid kernel extreme learning machine(HKELM).Finally,the optimized HKELM is used to locate the target detected by the skywave radar.The results show that the accuracy and stability of improved sparrow search algorithm are not only superior to the HKELM location model which is optimized by the basic sparrow search algorithm,but also stronger than the extreme learning machine(ELM)location model.In other words,the effectiveness of the method is proved.