改进鲸鱼优化算法在无源时差定位中的应用  

Application of improved whale optimization algorithm inTDOA passive localization

作　　者：陈光武[1,2] 佘一鸣 杨菊花 任超[1,2] CHEN Guangwu;SHE Yiming;YANG Juhua;REN Chao(Automatic Control Institute,Lanzhou Jiaotong University,Lanzhou 730070,China;Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动控制研究所,甘肃兰州730070 [2]甘肃省高原交通信息工程及控制重点实验室,甘肃兰州730070

出　　处：《传感器与微系统》2022年第3期150-153,157,共5页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(61863024);甘肃省高等学校科研资助项目(2018C-11,2018A-22)。

摘　　要：针对到达时间差(TDOA)非线性方程求解的数学问题,采用了一种改进鲸鱼优化群体智能算法(IWOA)。利用时间差值和空间位置的关系构建时空定位的三维数学模型,通过对最大似然函数进行优化,对初始种群以及适应度函数进行调整,在提升算法准确性的同时,进一步提升全局最优寻解能力,加速算法的收敛速度,提升算法的效率。该算法结构简单,鲁棒性较好,收敛过程既稳定又高效,而且定位精度较其他算法有所提升。仿真结果表明:改进鲸鱼优化算法在三维时空中可以高效、快速地收敛,较其他智能算法有更好的精度和收敛速度。To solve the mathematical problem of solving nonlinear equations involved in time difference of arrival(TDOA),using a new swarm intelligence algorithm called improved whale optimization algorithm(IWOA).using the relationship between the time and the position to build a three-dimensional mathematical model of space-time positioning.by optimizing the maximum likelihood function and adjusting the initial population and fitness function,While improving the accuracy of the algorithm,it also improves the global optimal solution ability,It accelerates the convergence speed of the algorithm and improves the efficiency of the algorithm.The algorithm has a simple structure,good robustness,and the convergence process is both stable and efficient,and the positioning accuracy is improved compared to other algorithms.The simulation results show that the improved whale optimization algorithm can efficiently and quickly converge in three-dimensional space-time,and has better accuracy and convergence speed than other intelligent algorithms.

关 键 词：改进鲸鱼优化算法 到达时间差 时空定位模型 适应度函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN929[自动化与计算机技术—计算机科学与技术]