基于改进鲸鱼优化的地面短基线单站无源定位方法
Ground Short-baseline Single-station Passive Location Method Based on Improved Whale Optimization
蒙淑娇 1晋良念2
作者信息
- 1. 桂林电子科技大学信息与通信学院,广西桂林 541004
- 2. 桂林电子科技大学信息与通信学院,广西桂林 541004;广西无线宽带通信与信号处理重点实验室,广西桂林 541004
- 折叠
摘要
针对粒子群优化(Particle Swarm Optimization,PSO)算法在解决常规地面单站无源定位系统性能受天线布阵形式约束影响时因局部收敛而导致定位精度低的问题,提出了一种基于改进鲸鱼优化算法(Improved Whale Optimization Algorithm,IWOA)的地面短基线单站无源定位法.利用阵元接收到的相位差信息构建代价函数,通过引入测向信息有效缩小算法的搜索范围,对算法的概率因子和收敛因子进行修正,提升算法计算速率,同时对算法的随机搜索方程进行改进并引入重启机制和局部精细搜索方式,在提升算法搜索能力的同时避免算法陷入局部最优.定位仿真结果表明,该算法在性能稳定性上优于PSO,相比于常规地面单站无源定位方法在定位精度上有了明显提升.
Abstract
Considering the low positioning accuracy caused by local convergence of Particle Swarm Optimization(PSO)algorithm when addressing the problem that the performance of conventional ground single-station passive positioning system is affected by antenna array constraints,a short-baseline single-station passive location method based on Improved Whale Optimization Algorithm(IWOA)is proposed.The phase difference information received by the array is used to construct the cost function.By introducing the direction finding information,the search scope of the algorithm is effectively reduced,and the probability factor and convergence factor of the algorithm are modified to improve the calculation rate of the algorithm.Meanwhile,the random search equation of the algorithm is improved and the restart mechanism and local fine search mode are introduced to prevent the algorithm from falling into the local optimal while improving the search ability of the algorithm.The simulation results show that the proposed algorithm is better than PSO in terms of performance stability,and the positioning accuracy is significantly improved compared with the conventional ground single-station passive positioning method.
关键词
无源定位/固定单站定位/鲸鱼优化/重启机制/局部精细搜索Key words
passive localization/fixed-single-station positioning/whale optimization/restart mechanism/local fine search引用本文复制引用
基金项目
广西创新驱动发展专项(桂科AA21077008)
广西自然科学基金(2023GXNSFAA026060)
八桂学者专项(2019A51)
出版年
2024
国家科技期刊平台
NSTL
万方数据