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基于单声矢量传声器虚拟扩展的多机动声目标跟踪算法

张君 鲍明 赵静 陈志菲 杨建华

张君, 鲍明, 赵静, 陈志菲, 杨建华. 基于单声矢量传声器虚拟扩展的多机动声目标跟踪算法. 自动化学报, 2023, 49(2): 383−398 doi: 10.16383/j.aas.c220172
引用本文: 张君, 鲍明, 赵静, 陈志菲, 杨建华. 基于单声矢量传声器虚拟扩展的多机动声目标跟踪算法. 自动化学报, 2023, 49(2): 383−398 doi: 10.16383/j.aas.c220172
Zhang Jun, Bao Ming, Zhao Jing, Chen Zhi-Fei, Yang Jian-Hua. Multi-maneuvering acoustic targets tracking algorithm based on virtual extension of single acoustic vector sensor. Acta Automatica Sinica, 2023, 49(2): 383−398 doi: 10.16383/j.aas.c220172
Citation: Zhang Jun, Bao Ming, Zhao Jing, Chen Zhi-Fei, Yang Jian-Hua. Multi-maneuvering acoustic targets tracking algorithm based on virtual extension of single acoustic vector sensor. Acta Automatica Sinica, 2023, 49(2): 383−398 doi: 10.16383/j.aas.c220172

基于单声矢量传声器虚拟扩展的多机动声目标跟踪算法

doi: 10.16383/j.aas.c220172
基金项目: 国家自然科学基金(12174314)资助
详细信息
    作者简介:

    张君:西北工业大学自动化学院博士研究生. 主要研究方向为阵列信号处理. E-mail: zhangjun_2018@mail.nwpu.edu.cn

    鲍明:中国科学院声学研究所研究员. 主要研究方向为矢量传感器与处理, 智能信号处理. E-mail: baoming@mail.ioa.ac.cn

    赵静:中国科学院声学研究所特别研究助理. 主要研究方向为矢量传感器设计, 声学测量. E-mail: zhaojing@mail.ioa.ac.cn

    陈志菲:中国科学院声学研究所副研究员. 主要研究方向为传感器阵列处理, 声源定位和声学测量.E-mail: chenzhifei@mail.ioa.ac.cn

    杨建华:西北工业大学自动化学院教授. 主要研究方向为传感器信号处理, 检测与控制技术, 仿生机器人和生物医学图像处理. 本文通信作者. E-mail: yangjianhua@nwpu.edu.cn

Multi-maneuvering Acoustic Targets Tracking Algorithm Based on Virtual Extension of Single Acoustic Vector Sensor

Funds: Supported by National Natural Science Foundation of China (12174314)
More Information
    Author Bio:

    ZHANG Jun Ph.D. candidate at the School of Automation, Northwestern Polytechnical University. Her main research interest is array signal processing

    BAO Ming Researcher at the Institute of Acoustics, Chinese Academy of Sciences. His research interest covers vector sensor and processing, intelligent signal processing

    ZHAO Jing Special research assistant at the Institute of Acoustics, Chinese Academy of Sciences. Her research interest covers vector sensor design and acoustic measurement

    CHEN Zhi-Fei Associate researcher at the Institute of Acoustics, Chinese Academy of Sciences. His research interest covers sensor array processing, source localization, and acoustic measurement

    YANG Jian-Hua Professor at the School of Automation, Northwestern Polytechnical University. Her research interest covers sensor signal processing, detection and control technology, bionic robot, and biomedical image processing. Corresponding author of this paper

  • 摘要: 为解决单声矢量传声器(Acoustic vector sensor, AVS)可跟踪声目标数目少、跟踪性能差的问题, 提出了基于AVS虚拟扩展的多机动声目标跟踪算法. 首先, 引入高阶累积量预处理过程并建立高阶似然函数, 不仅能够抑制高斯噪声、提高估计精度, 还可通过AVS的虚拟扩展增加可跟踪目标数目. 然后, 在边缘化$\delta$广义标签多伯努利(Marginalized $\delta$-generalized label multi-bernoulli, M$\delta$-GLMB)滤波框架下, 提出了基于累积量的增广运动模型状态的M$\delta$-GLMB (Cumulants-based augumented motion model state M$\delta$-GLMB, Cum-AMMS-GLMB)算法. 算法引入多种运动模型, 并将表征不同模型的索引标号作为目标状态的增广参数, 通过各模型间的加权混合获取优于单一运动模型的跟踪性能. 除此之外, 算法的序贯蒙特卡洛(Sequential Monte Carlo, SMC)实现过程中, 依据高阶预处理获得的归一化空间谱拟合检测概率函数, 抑制了杂波向可用粒子扩展, 进一步增强了高似然区域的粒子. 最后, 推导了AVS目标跟踪的后验克拉美罗下界(Posterior cram$\acute{e}$r-rao lower bound, PCRLB), 并通过仿真实验验证了算法的量测噪声抑制能力和声目标跟踪性能.
  • 图  1  不同信噪比下的归一化高阶似然函数示例

    Fig.  1  Example of normalized higher-order spatial spectrum under different SNR

    图  2  检测概率模型

    Fig.  2  Detection probability model

    图  3  Cum-AMMS-GLMB算法的多声目标跟踪结果

    Fig.  3  Multiple acoustic target tracking results of Cum-AMMS-GLMB algorithm

    图  4  双声目标情况下不同算法的估计结果

    Fig.  4  The estimation results of different algorithms in the case of two acoustic targets

    图  5  不同信噪比下PCRLB和各算法的RMSE估计结果

    Fig.  5  PCRLB and RMSE estimation results of each algorithm under different SNR

    图  6  不同$\sigma_w$下PCRLB和各算法的RMSE估计结果

    Fig.  6  PCRLB and RMSE estimation results of each algorithm under different $\sigma_w$

    图  7  半消声室声目标跟踪实验

    Fig.  7  Acoustic targets tracking experiment in semi-anechoic chamber

    图  8  目标3一直存在情况下的跟踪结果

    Fig.  8  Tracing result when the target 3 is always present

    图  9  目标3突然出现情况下的跟踪结果

    Fig.  9  Tracing result when target 3 suddenly appears

    图  10  目标3突然消失情况下的跟踪结果

    Fig.  10  Tracing result when target 3 suddenly disappears

    表  1  各时间段对应的运动模型

    Table  1  Movement model corresponding to each time period

    时间 1~15 s 16~30 s 31~40 s 41~50 s
    运动模型 CV模型 CA模型
    $0.1^\circ/{\rm s}^2$
    CT模型
    $ \omega=-2\pi/180$
    CV模型
    下载: 导出CSV

    表  2  声目标的运动状态和幸存时间

    Table  2  Motion state and survival time of acoustic targets

    声目标 初始状态 存在时间
    目标1 $\mathrm{DOA}:\ \{30^\circ,30^\circ\}$, 速度$:\ \{1^\circ/{\rm s},0.05^\circ/{\rm s}\}$ 1~50 s
    目标2 $\mathrm{DOA}:\ \{300^\circ,80^\circ\}$, 速度$:\ \{-1^\circ/{\rm s},1^\circ/{\rm s}\}$ 1~50 s
    目标3 $\mathrm{DOA}:\ \{200^\circ,100^\circ\}$, 速度$:\ \{0^\circ/{\rm s},0.1^\circ/{\rm s}\}$ 10~20 s
    目标4 $\mathrm{DOA}:\ \{150^\circ,40^\circ\}$, 速度$:\ \{1^\circ/{\rm s},0^\circ/{\rm s}\}$ 1~50 s
    目标5 $\mathrm{DOA}:\ \{90^\circ,80^\circ\}$, 速度$:\ \{1^\circ/{\rm s},0.5^\circ/{\rm s}\}$ 25~50 s
    下载: 导出CSV

    表  3  各对比算法的似然函数、滤波器的区别

    Table  3  The difference between the likelihood function and the filter of each comparison algorithm

    对比算法 似然函数 滤波器
    Cum-AMMS-GLMB 式(7)高阶似然 M$\delta$-GLMB
    Cov-GLMB[26] MUSIC空间谱(指数加权) $\delta$-GLMB
    CBMeMBer[34] MUSIC空间谱(指数加权) CBMeMBer
    Cum-CBMeMBer 式(7)高阶似然 CBMeMBer
    下载: 导出CSV
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  • 收稿日期:  2022-03-12
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  • 网络出版日期:  2023-01-18
  • 刊出日期:  2023-02-20

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