基于动态Voronoi分区的多舰协同搜索方法

蒋海军; 张翼超; 孙雅萍; 陈宏昆

doi:10.11993/j.issn.2096-3920.2025-0123

基于动态Voronoi分区的多舰协同搜索方法

doi: 10.11993/j.issn.2096-3920.2025-0123

1.
中国人民解放军92899部队, 浙江宁波, 315200
2.
中国人民解放军92192部队, 浙江宁波, 315200

详细信息

作者简介:
蒋海军(1982-), 男, 硕士, 高级工程师, 研究方向为水声信号处理

中图分类号: TJ630.34; U647.7
计量
- 文章访问数: 18
- HTML全文浏览量: 11
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2025-09-09
- 修回日期: 2025-10-05
- 录用日期: 2025-10-09
- 网络出版日期: 2026-01-15

Multi-ship cooperative search method based on dynamic Voronoi partitioning

1.
92899^thUnit, The People’s Liberation Army of China, Ningbo 315200, China
2.
92192^th Unit, The People’s Liberation Army of China, Ningbo 315200, China

摘要

摘要: 传统多舰协同检查搜索主要采用固定分区且未考虑目标规避, 存在发现概率低、贴近实战不足的问题。文章提出一种基于动态Voronoi分区与多源信息联合决策的多舰协同搜索方法, 该方法以贝叶斯概率框架为基础, 融合声呐探测模型与目标运动扩散模型, 构建并动态更新目标位置的概率分布图。通过Voronoi图自适应划分搜索区域, 为各舰船划定责任分区, 实现任务空间分布式部署, 可显著降低区域冗余覆盖, 同时消除搜索盲区。针对“探索-利用”策略的阶段化适配需求(前期侧重探索、后期侧重利用), 设计多源信息融合评分模型, 将目标存在概率、区域未搜索程度及局部信息熵纳入综合计算, 并构建权重随搜索进度调整机制, 使搜索策略随任务进程动态调整, 进而指导舰船确定最优搜索目标点。在目标主动规避的对抗场景下, 将所提方法与固定分区的“弓”字形面积覆盖法、粒子群最大概率航向优化法进行对比, 通过1 000次蒙特卡洛仿真表明, 所提方法在多舰协同搜索任务中能显著缩短发现目标时间, 提高统计意义上的发现目标概率, 在对抗环境下具备良好的真实性与可扩展性。
- 多舰协同 /
- 搜索 /
- Voronoi分区 /
- 信息熵 /
- 贝叶斯估计
Abstract: Traditional multi-ship cooperative search often uses fixed partitioning and ignores target evasion, leading to low detection probability and poor realism. This paper proposes a dynamic Voronoi-based method with multi-source information fusion. Built on a Bayesian framework incorporating sonar detection and target diffusion models, it dynamically updates the probability distribution of the target’s location. Adaptive Voronoi partitioning enables distributed task allocation, reducing redundant coverage and eliminating blind spots. A multi-source scoring model integrating presence probability, unexplored area, and local entropy, with time-varying weights, balances exploration and exploitation throughout the search. Compared with fixed-area sweep and particle-swarm-based methods in 1 000 Monte Carlo simulations under evasion scenarios, the proposed method significantly reduces target acquisition time and improves detection probability, demonstrating superior realism and scalability in adversarial environments.
- multi-ship cooperation /
- search /
- Voronoi partitioning /
- information entropy /
- Bayesian estimation

HTML全文

图 1 Voronoi分区示意图

Figure 1. Schematic diagram of Voronoi partitioning

下载: 全尺寸图片幻灯片

图 2 $ t=0.5 $ h搜索态势图(联合发现概率0.00)

Figure 2. Search situation map at t=0.5 h (joint detection probability: 0.00)

下载: 全尺寸图片幻灯片

图 3 $ t=20 $ h搜索态势图(联合发现概率0.00)

Figure 3. Search situation map at t=20 h (joint detection probability: 0.00)

下载: 全尺寸图片幻灯片

图 4 $ t=41 $ h搜索态势图(联合发现概率1.00)

Figure 4. Search situation map at t=41 h (joint detection probability: 1.00)

下载: 全尺寸图片幻灯片

图 5 目标规避舰船过程轨迹图

Figure 5. Trajectory diagram of target evading warships process

下载: 全尺寸图片幻灯片

图 6 舰船与目标轨迹图

Figure 6. Track map of warships and target

下载: 全尺寸图片幻灯片

图 7 舰船与目标轨迹图

Figure 7. Track map of warships and target

下载: 全尺寸图片幻灯片

图 8 所提方法发现目标时间分布

Figure 8. Time distribution of target detection by the proposed method

下载: 全尺寸图片幻灯片

图 9 不同搜索策略累积发现概率随时间变化曲线

Figure 9. Curve of target detection probability by different search strategies over time

下载: 全尺寸图片幻灯片

表 1 不同搜索策略对比情况

Table 1. Comparison of different search strategies

策略	发现概率/%	平均发现时间/h	平均每次运行时间/s
动态Voronoi分区多源协同	77.9	21.4	3.88
粒子群最大概率航向优化法	75.0	23.4	45.93
“弓”字形面积覆盖法	63.3	35.0	0.001

下载: 导出CSV

参考文献(20)

[1]	王磊, 吴福初. 基于线列阵声纳的水面舰艇检查搜潜仿真[J]. 火力与指挥控制, 2011, 36(11): 91-95. WANG L, WU F C. Discussion about the efficiency of surface ships checking antisubmarine which using sonar buoy[J]. Fire Control & Command Control, 2011, 36(11): 91-95.
[2]	高永琪, 马威强, 张林森, 等. 分布式AUV协同搜索方法[J]. 系统工程与电子技术, 2022, 44(5): 1674-1675. GAO Y Q, MA W Q, ZHANG L S, et al. Distributed multi-AUVs cooperative search method[J]. Systems Engineering and Electronics, 2022, 44(5): 1674-1675.
[3]	冯西安, 寇思玮, 岳玲. 水下移动平台网络化协同探测技术发展[J]. 应用声学, 2019, 38(4): 1674-1675. doi: 10.11684/j.issn.1000-310X.2019.04.006 FENG X A, KOU S W, YUE L. Development of networked collaborative detection technology for underwater mobile platforms[J]. Journal of Applied Acoustics, 2019, 38(4): 1674-1675. doi: 10.11684/j.issn.1000-310X.2019.04.006
[4]	付乐乐, 陈宏, 巩伟杰. 基于改进蚁群算法的水下机器人路径规划[J]. 自动化与仪表, 2022, 37(4): 46-50. doi: 10.3969/j.issn.1009-0134.2022.12.039 FU L L, CHEN H, GONG W J. Path planning of underwater robots based on improved ant colony algorithm[J]. Automation & Instrumentation, 2022, 37(4): 46-50. doi: 10.3969/j.issn.1009-0134.2022.12.039
[5]	邹佳运, 曲泓玥, 陈志鹏. 大规模水下滑翔机集群区域覆盖探测路径规划[J]. 水下无人系统学报, 2021, 29(1): 23-29. doi: 10.11993/j.issn.2096-3920.2021.01.004 ZOU J Y, QU H Y, CHEN Z P. Path planning of a large-scale underwater glider swarm area coverage detection[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 23-29. doi: 10.11993/j.issn.2096-3920.2021.01.004
[6]	王飞, 陈芳香. 基于多目标粒子群算法的水面舰艇编队搜潜阵型参数优化方法[J]. 声学与电子工程, 2025, 158(2): 7-11. WANG F, CHEN F X. Optimization method for anti-submarine formation parameters of surface ship formation based on multi-objective particle swarm algorithm[J]. Acoustics and Electronics Engineering, 2025, 158(2): 7-11.
[7]	JIA Q, XU H. Research on cooperative area search of multiple underwater robots based on the prediction of initial target information[J]. Ocean Engineering, 2019, 172: 660-670. doi: 10.1016/j.oceaneng.2018.12.035
[8]	廖燕荣, 姜可宇. 多舰协同分区搜潜动态航路规划[J]. 舰船电子工程, 2022, 42(7): 23-27. doi: 10.3969/j.issn.1672-9730.2022.07.007 LIAO Y R, JIANG K Y. Dynamic route planning of partitioned searching of multiple surface ships[J]. Ship Electronic Engineering, 2022, 42(7): 23-27. doi: 10.3969/j.issn.1672-9730.2022.07.007
[9]	陈捷, 刘海颖, 李志豪, 等. 基于改进Voronoi图的集群无人机区域覆盖方法[J]. 电子设计工程, 2023, 31(6): 31-32. CHEN J, LIU H Y, LI Z H, et al. Multi-UAV area coverage method based on improved Voronoi diagram[J]. Electronic Design Engineering, 2023, 31(6): 31-32.
[10]	高永琪, 王鹏, 马威强, 等. 改进头脑风暴算法在多AUV协同搜索动态目标中的应用[J]. 国防科技大学学报, 2024, 46(6): 203-209. GAO Y Q, WANG P, MA W Q, et al. Application of improved brain storm optimization algorithm in multi-AUV cooperative search moving targets[J]. Journal of National University of Defense Technology, 2024, 46(6): 203-209.
[11]	黄杰, 孙伟, 高渝. 双属性概率图优化的无人机集群协同目标搜索[J]. 系统工程与电子技术, 2020, 42(1): 118-127. doi: 10.3969/j.issn.1001‐506X.2020.01.16 HUANG J, SUN W, GAO Y. Cooperative searching for the multi-UAVs based on dual-attribute probabilistic model optimization[J]. Systems Engineering and Electronics, 2020, 42(1): 118-127. doi: 10.3969/j.issn.1001‐506X.2020.01.16
[12]	张哲璇, 龙腾, 徐广通, 等. 重访机制驱动的多无人机协同动目标搜索方法[J]. 航空学报, 2020, 41(5): 220-232. ZHANG Z X, LONG T, XU G T, et al. Revisit mechanism driven multi-UAV cooperative search planning method for moving target[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 220-232.
[13]	MISHRA M, AN W et al. Context-aware decision support for antisubmarine warfare mission planning within a dynamic environment[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(1): 318-335.
[14]	WANG C Q, MEI D, WANG Y, et al. Task allocation for Multi-AUV system: A review[J]. Ocean Engineering, 2022, 266: 112-119.
[15]	张宁, 寇小明, 李斌, 等. 基于遗传算法的应召搜潜路径优化[J]. 水下无人系统学报, 2023, 31(2): 244-251. doi: 10.11993/j.issn.2096-3920.2022-0002 ZHANG N, KOU X M, LI B, et al. Route optimization of on call submarine search based on genetic algorithm[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 244-251. doi: 10.11993/j.issn.2096-3920.2022-0002
[16]	赵苗, 高永琪, 吴笛霄, 等. 复杂海战场环境下AUV全局路径规划方法[J]. 国防科技大学学报, 2021, 43(1): 41-48. doi: 10.11887/j.cn.202101006 ZHAO M, GAO Y Q, WU D X, et al. AUV global path planning method in complex sea battle field environment[J]. Journal of National University of Defense Technology, 2021, 43(1): 41-48. doi: 10.11887/j.cn.202101006
[17]	张美燕, 蔡文郁. 基于多AUV间任务协作的水下多目标探测路径规划[J]. 传感技术学报, 2018, 31(7): 1102-1106. ZHANG M Y, CAI W Y. Underwater targets tracking path planning based on task cooperation of multiple AUVs[J]. Chinese Journal of Sensors and Actuators, 2018, 31(7): 1101-1107.
[18]	冯豪博, 胡桥, 赵振轶. 基于精英族系遗传算法的AUV 集群路径规划[J]. 系统工程与电子技术, 2022, 44(7): 2251-62. FENG H B, HU Q, ZHAO Z Y. AUV swarms path planning based on elite family genetic algorithm[J]. Systems Engineering and Electronics, 2022, 44(7): 2251-2262.
[19]	彭辉, 沈林成, 霍霄华. 多UAV协同区域覆盖搜索研究[J]. 系统仿真学报, 2007, 19(11): 2472-2476. doi: 10.3969/j.issn.1004-731X.2007.11.022 PENG H, SHEN L C, HUO X H. Research on multiple UAV cooperative area coverage searching[J]. Journal of System Simulation, 2007, 19(11): 2472-2476. doi: 10.3969/j.issn.1004-731X.2007.11.022
[20]	WAITE A D. 实用声纳工程[M]. 王德石, 译. 3版. 北京: 电子工业出版社, 2004: 155-156.