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基于水下传感器网络的目标跟踪技术研究现状与展望

刘妹琴 韩学艳 张森林 郑荣濠 兰剑

刘妹琴, 韩学艳, 张森林, 郑荣濠, 兰剑. 基于水下传感器网络的目标跟踪技术研究现状与展望. 自动化学报, 2021, 47(2): 235−251 doi: 10.16383/j.aas.c190886
引用本文: 刘妹琴, 韩学艳, 张森林, 郑荣濠, 兰剑. 基于水下传感器网络的目标跟踪技术研究现状与展望. 自动化学报, 2021, 47(2): 235−251 doi: 10.16383/j.aas.c190886
Liu Mei-Qin, Han Xue-Yan, Zhang Sen-Lin, Zheng Rong-Hao, Lan Jian. Research status and prospect of target tracking technologies via underwater sensor networks. Acta Automatica Sinica, 2021, 47(2): 235−251 doi: 10.16383/j.aas.c190886
Citation: Liu Mei-Qin, Han Xue-Yan, Zhang Sen-Lin, Zheng Rong-Hao, Lan Jian. Research status and prospect of target tracking technologies via underwater sensor networks. Acta Automatica Sinica, 2021, 47(2): 235−251 doi: 10.16383/j.aas.c190886

基于水下传感器网络的目标跟踪技术研究现状与展望

doi: 10.16383/j.aas.c190886
基金项目: 国家自然科学基金(61673345, U1609204, U1809212, U1809202), 浙江省自然科学基金(LZ19F030002) 资助
详细信息
    作者简介:

    刘妹琴:浙江大学工业控制技术国家重点实验室和电气工程学院教授. 主要研究方向为人工智能理论与应用, 海洋信息技术, 信息融合, 非线性控制理论与应用. 本文通信作者. E-mail: liumeiqin@zju.edu.cn

    韩学艳:浙江大学电气工程学院博士研究生. 主要研究方向为目标跟踪, 多传感器网络, 信息融合, 方位估计. E-mail: xueyanhan@zju.edu.cn

    张森林:浙江大学电气工程学院教授. 主要研究方向为水下无线传感器网络, 信息融合, 纺织自动化. E-mail: slzhang@zju.edu.cn

    郑荣濠:浙江大学电气工程学院副教授. 主要研究方向为多机器人系统, 协同控制与优化, 海洋信息技术. E-mail: rzheng@zju.edu.cn

    兰剑:西安交通大学自动化科学与工程学院教授. 主要研究方向为信息融合理论与技术. E-mail: lanjian@mail.xjtu.edu.cn

Research Status and Prospect of Target Tracking Technologies via Underwater Sensor Networks

Funds: Supported by National Natural Science Foundation of China (61673345, U1609204, U1809212, U1809202) and Zhejiang Provincial Natural Science Foundation (LZ19F030002)
  • 摘要: 水下目标跟踪在海洋资源的开发利用以及国家安全的防御等方面都具有广泛的应用价值和重要的战略意义. 基于水下传感器网络(Underwater sensor networks, USNs)的目标跟踪技术凭借其覆盖范围广、观测时间长和实时融合等优势已经成为一个新的研究热点. 本文针对基于USNs的目标跟踪关键技术的基本思想、研究进展、应用及局限性进行了综述, 主要从以下几个角度对其展开论述: USNs的建设现状、系统组成及其分类、目标跟踪系统模型、单目标跟踪技术、多目标跟踪技术以及能效优化措施. 最后, 本文不仅指出了基于USNs的目标跟踪研究目前存在的主要挑战, 并对该领域的未来发展方向进行了展望.
  • 图  1  基于USNs的目标跟踪技术

    Fig.  1  Target tracking technologies via USNs

    图  2  USNs的系统组成

    Fig.  2  System composition of USNs

    图  3  传感器节点的基本架构

    Fig.  3  Basic architecture of sensor node

    图  4  同步量测

    Fig.  4  Synchronous measurement

    图  5  集中式融合过程

    Fig.  5  The centralized fusion process

    图  6  分布式融合过程

    Fig.  6  The distributed fusion process

    图  7  异步量测

    Fig.  7  Asynchronous measurement

    图  8  多目标运动模型

    Fig.  8  Multi-target motion model

    图  9  多目标量测模型

    Fig.  9  Multi-target measurement model

    图  10  基于数据关联的多目标跟踪

    Fig.  10  Multi-target tracking based on data association

    图  11  基于RFS的多目标跟踪系统模型

    Fig.  11  Multi-target tracking system model via RFS

    表  1  各种数据关联算法的比较

    Table  1  Comparison of various data association algorithms

    方法优点缺点适用场景
    NN/GNN计算量小, 实时性较好, 工程实现简单抗干扰能力弱, 在量测信息密度较大或环境因素过于复杂时, 性能较差仅适用于信噪比较高, 目标密度较小的场合
    PDA计算量和存储量较小, 易于工程实现在密集杂波或多目标环境中容易产生误跟或丢失等现象适用于杂波环境下的单目标或跟踪门不重叠的多目标环境
    JPDA不需要任何关于目标和杂波的先验信息计算量和存储量大, 实时性差, 工程实现困难, 量测数和目标数较大时存在组合爆炸现象适用于密集多目标和多杂波、目标数目恒定已知的情况
    MHT在理想条件下是处理数据关联的最优算法计算量大, 过于依赖目标和杂波的先验知识, 假设数量随量测数和目标数呈指数级增长适用于密集多目标和多杂波、目标数未知且时变的情况
    PMHT批处理方法, 计算量随目标数的增长而呈线性增长目标的后验概率函数可能会收敛到局部最大值, 算法对初始值比较敏感适用于密集多目标和多杂波、目标数未知且时变的情况
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-24
  • 录用日期:  2020-02-16
  • 网络出版日期:  2021-02-26
  • 刊出日期:  2021-02-26

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