基于改进粒子群优化的水下地形辅助导航方法

摘要

为了提高传统地形匹配算法的定位精度,提出一种基于改进粒子群优化的水下地形辅助导航定位算法.该算法以SINS的指示位置为中心构造搜索区域,对二维粒子群进行初始化,利用实时水深测量序列与待匹配序列之间的平均Hausdorff距离作为适应度函数,在线性递减权重的基础上引入收敛因子对粒子的速度和位置进行约束更新,改善粒子"早熟"问题.在某海图内进行了水下地形匹配仿真实验,结果表明:初始位置误差大小不影响改进PSO算法的定位精度和匹配速度;当水下航行器初始位置误差较大时,与TERCOM算法相比,改进PSO算法的匹配精度提高了近5倍,匹配耗时缩减了近10倍.%In order to improve the positioning accuracy of traditional terrain matching algorithm, a positioning algorithm of underwater terrain aided navigation (TAN) based on improved particle swarm optimization (PSO) is proposed. The algorithm constructs the search region centered at the SINS indication position and initializes the two-dimensional particle swarm. The mean Hausdorff distance between the sequence of real-time water depth measurements and the sequence to be matched is used as the fitness function. Based on the linear reduction weight, the convergence factor is introduced to update the constraint on the velocity and the position of the particle in order to improve the particle "precocious" problem. The underwater terrain matching simulation experiments are carried out based on a sea chart, and the results show that the positioning accuracy and the matching speed of the improved PSO algorithm is not affected by the initial position error. When the initial position error is large, the matching accuracy of the improved PSO algorithm is increased by 5 times and the matching time is reduced by nearly 10 times, compared with the terrain contour matching algorithm.