基于声纳搜索累积探测概率的平台路径优化方法

doi:10.16182/j.issn1004731x.joss.23-0953

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (11): 2674-2683.doi: 10.16182/j.issn1004731x.joss.23-0953

• 研究论文 • 上一篇

基于声纳搜索累积探测概率的平台路径优化方法

卫翔¹, 刘星璇¹, 付殿峥², 杨天吉², 杨家轩¹

^1.海军潜艇学院，山东青岛 266000
^2.中国科学院沈阳自动化研究所，辽宁沈阳 110016

收稿日期:2023-07-28 修回日期:2023-11-01 出版日期:2024-11-13 发布日期:2024-11-19
通讯作者: 刘星璇
第一作者简介:卫翔(1980-)，男，研究员，博士，研究方向为作战实验。
基金资助:
国家自然科学基金青年基金(62003335)

Platform Path Optimization Method Based on Cumulative Detection Probability of Sonar Search

Wei Xiang¹, Liu Xingxuan¹, Fu Dianzheng², Yang Tianji², Yang Jiaxuan¹

^1.PLA Naval Submarine Academy, Qingdao 266000, China
^2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

Received:2023-07-28 Revised:2023-11-01 Online:2024-11-13 Published:2024-11-19
Contact: Liu Xingxuan

摘要/Abstract

摘要：

针对面向移动目标的移动搜索平台最优路径研究不足问题，提出一种基于累积搜索概率理论的移动搜索平台路径优化方法。基于传感器性能评价的重要标准之一的累积探测概率（cumulative detection probability，CDP），利用时序相关性模型，即（ $λ$ ， $σ$ ）过程模型，构造单峰CDP计算公式。构建一组目标运动想定，利用贝叶斯后验概率修正目标想定轨迹概率和不同时刻下的CDP。以搜索完成时CDP最大以及CDP达到目标水平时间最短为多目标，在连续时间与连续空间中实现高效搜索，构建面向声纳搜索移动目标的路径优化模型，通过多目标遗传算法给出优化解。与随机搜索模式下的CDP结果比对可以发现，本文方法可以获得更高的CDP，比单目标优化结果所得到的搜索方案具有更高的效率。

关键词: 声纳搜索, 累积探测概率, 路径优化, 多目标优化, 遗传算法

Abstract:

To address the lack of research on the optimal path of mobile search platform to search for moving targets, this paper proposes a path optimization method of mobile search platform based on cumulative search probability theory. Based on the cumulative detection probability (CDP), one of the important criteria of sensor performance evaluation, a single-peak CDP calculation formula is constructed by using a time series correlation model, namely the ( $λ$ , $σ$ ) process model. A set of target motion scenarios are constructed, and the trajectory probability of target scenarios and their CDP at different time are corrected by Bayesian posterior probability. Considering the maximum CDP at the completion of the search and the shortest time for CDP to reach the target level as multi-objectives, a path optimization model for sonar searching moving targets is constructed to realize efficient search in continuous time and continuous space, and the optimal solution is provided by utilizing the multi-objective genetic algorithm. Through comparison with CDP results in random search mode, it can be found that the optimization method proposed in this paper can obtain a higher CDP, and the search scheme obtained has better efficiency advantages than that obtained from single-objective optimization.

Key words: sonar search, cumulative detection probability(CDP), path optimization, multi-objective optimization, genetic algorithm

中图分类号:

TP391.9

卫翔,刘星璇,付殿峥等 . 基于声纳搜索累积探测概率的平台路径优化方法[J]. 系统仿真学报, 2024, 36(11): 2674-2683.

Wei Xiang,Liu Xingxuan,Fu Dianzheng,et al . Platform Path Optimization Method Based on Cumulative Detection Probability of Sonar Search[J]. Journal of System Simulation, 2024, 36(11): 2674-2683.

图/表 10

表1

符号含义列表

序号	符号	含义
1	P	声纳瞬时搜索概率
2	r	搜索平台与目标的距离
3	r_max	理论最大搜索距离
4	v	搜索平台与目标的相对移动速度
5	x	搜索平台与目标运动轨迹的最小距离
6	Δt	声纳扫描的间隔
7	P_k	第k次扫视的瞬时搜索概率
8	F	一次航路捷径的累积探测概率
9	λ	信号跳跃的速率
10	v_t	目标运动速度
11	ps_i	第i个航路轨迹权重
12	v_s	搜索平台运动速度
13	S_j	搜索方案中的航路点
14	T_j	搜索平台运动到航路点的时间
15	θ	搜索平台与目标航向的夹角
16	ps_i^*	贝叶斯后验轨迹权重
17	$c d p t = T j$	到T_j 时刻搜索方案的累积探测概率
18	t^*	实现cdp≥0.9的最短时间
19	w	任务区域宽度
20	s	任务区域长度
21	N	搜索方案的最大折数
22	η	最大CDP未达到0.9的惩罚因子

表1

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基于声纳搜索累积探测概率的平台路径优化方法

Platform Path Optimization Method Based on Cumulative Detection Probability of Sonar Search

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