针对无人航行器（Unmanned Underwater Vehicle, UUV）在潜搜索中目标运动状态难以准确描述、传统搜索方法难以保证有限搜索时间内发现概率最大的问题，本文提出一种基于探测概率的UUV搜潜路径规划方法。首先，将搜索区域栅格化，建立基于隐马尔科夫模型（Hidden Markov Model, HMM）的目标运动模型，可实时更新目标的后验概率分布。然后，以探测概率为目标函数，采用改进的遗传算法对UUV搜索路径进行规划，使得在确定的搜索时间内对潜探测概率最大。在仿真实验中，通过与经典算例对比，验证了方法的正确性；通过与矩形搜索、随机搜索2种常用搜索方法的对比，验证了该方法的有效性。
Aiming at the status of the target is hard to describe accurately and the traditional submarine searching methods can’t get optimal detection probability, a submarine searching path planning method is provided for Unmanned Underwater Vehicle (UUV) based on detection probability. Firstly, the searching area is divided into grids and the target motion model is built based on Hidden Markov Model (HMM). Then, the submarine searching path panning is done by genetic algorithm (GA) with UUV to get largest detection probability which is taken as the objective function. In the simulation experiments, compared with classic example, the results illustrate the correctness of the method. Compared with two common methods of rectangle searching and random searching, the results showed the effectiveness of the method.
2024,46(9): 82-87 收稿日期：2023-06-28
DOI：10.3404/j.issn.1672-7649.2024.09.014
分类号：E925.2
作者简介：付留芳（1989 -），女，博士，讲师，研究方向为水面舰艇反潜
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