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基于分布式有限感知网络的多伯努利目标跟踪

吴孙勇 王力 李天成 孙希延 蔡如华 伍雯雯

吴孙勇, 王力, 李天成, 孙希延, 蔡如华, 伍雯雯. 基于分布式有限感知网络的多伯努利目标跟踪. 自动化学报, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c200481
引用本文: 吴孙勇, 王力, 李天成, 孙希延, 蔡如华, 伍雯雯. 基于分布式有限感知网络的多伯努利目标跟踪. 自动化学报, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c200481
Wu Sun-Yong, Wang Li, Li Tian-Cheng, Sun Xi-Yan, Cai Ru-Hua, Wu Wen-Wen. Multi-bernoulli target tracking based on distributed limited sensing network. Acta Automatica Sinica, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c200481
Citation: Wu Sun-Yong, Wang Li, Li Tian-Cheng, Sun Xi-Yan, Cai Ru-Hua, Wu Wen-Wen. Multi-bernoulli target tracking based on distributed limited sensing network. Acta Automatica Sinica, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c200481

基于分布式有限感知网络的多伯努利目标跟踪

doi: 10.16383/j.aas.c200481
基金项目: 国家自然科学基金(61861008,11661024), 广西自然科学基金(2016GXNSFAA380073), 广西研究生教育创新计划项目(2020YCXS084), 桂林电子科技大学数学与计算科学学院论文培优项目(2019YJSPY04)
详细信息
    作者简介:

    吴孙勇:桂林电子科技大学数学与计算科学学院教授, 2011年获西安电子科技大学信号与信息处理博士学位. 目前主要研究方向为多目标检测与跟踪, 阵列信号处理. E-mail: wusunyong121991@163.com

    王力:桂林电子科技大学数学与计算科学学院的学生, 主要研究方向是多目标检测与跟踪和多传感器信息融合. 本文通信作者. E-mail: wangli1581960594@163.com

    李天成:2013年获英国伦敦南岸大学博士学位, 2015年获西北工业大学博士学位. 现任西北工业大学自动化学院教授. 主要研究分布式信息融合, 协作移动机器人和目标检测, 跟踪和轨迹预测的数据驱动算法. E-mail: t.c.li@nwpu.edu.cn

    孙希延:桂林电子科技大学信息与通信工程学院教授. 主要研究方向为卫星通信, 卫星导航等. E-mail: sunxiyan1@163.com

    蔡如华:桂林电子科技大学数学与计算科学学院副教授, 主要研究方向为小波分析, 信号处理和粒子滤波. E-mail: ruhuac@guet.edu.cn

    伍雯雯:桂林电子科技大学数学与计算科学学院的学生. E-mail: wuwenwen202101@163.com

Multi-Bernoulli Target Tracking Based on Distributed Limited Sensing Network

Funds: Supported by National Natural Science Foundation of P. R. China (61861008,11661024), Guangxi Natural Science Foundation(2016GXNSFAA380073), Guangxi graduate education innovation plan project(2020YCXS084), Thesis training program of School of Mathematics and Computational Science, Guilin University of Electronic Technology(2019YJSPY04)
  • 摘要: 针对感知范围受限的分布式传感网多目标跟踪问题, 在多伯努利滤波跟踪理论基础上提出分布式视场互补多伯努利关联算术平均融合跟踪方法. 首先, 通过视场互补扩大传感器感知范围, 其中, 局部公共区域只互补一次以降低计算成本. 其次, 每个传感器分别运行局部多伯努利滤波器, 并将滤波后验结果与相邻传感器进行泛洪通信使得每个传感器获取多个相邻传感器的后验信息. 随后, 通过距离划分进行多伯努利关联, 将对应于同一目标的伯努利分量关联到同一个子集中, 并对每个关联子集进行算术平均融合完成融合状态估计. 仿真实验表明, 所提方法在有限感知范围的分布式传感器网络中能有效地进行多目标跟踪.
  • 图  1  有限传感范围分布式传感器网络

    Fig.  1  Distributed sensor networks with limited sensing range.

    图  2  分布式传感器网络与真实轨迹

    Fig.  2  Distributed sensor networks and real trajectories

    图  3  各传感器视场互补后滤波跟踪的TNOSPA

    Fig.  3  Tracking error TNOSPA of local sensors with complementary field of view

    图  4  M1情况下目标跟踪性能

    Fig.  4  Target tracking performance in M1

    图  5  M2情况下目标跟踪性能

    Fig.  5  Target tracking performance in M2

    图  6  第7个传感器跟踪性能对比结果

    Fig.  6  The sensor 7 tracks performance comparison results

    图  7  多传感器多伯努利滤波AA融合仿真效果

    Fig.  7  Multi-sensor multi-Bernoulli filter AA fusion simulation effect

    图  8  目标数为11的仿真效果

    Fig.  8  Simulation effect with the target number of 11

    图  9  本文方法在不同存活率下的跟踪性能

    Fig.  9  The tracking performance of this paper under different survival rates1

    图  10  本文方法在不同检测概率下的跟踪性能

    Fig.  10  The tracking performance of this paper under different detection probability

    图  11  不同方法的TNOSPA误差统计图

    Fig.  11  TNOSPA error statistics of different methods

    表  1  目标初始位置和存活时间

    Table  1  Target’s initial position and survival time

    目标出生位置出生时间/s死亡时间/s
    目标1[−596.14,−606.75]170
    目标2[307.38 693.2]1065
    目标3[692.7 206.8]2080
    目标4[700, 200]3060
    目标5[−603.9,−588.93]40100
    目标6[294.12,705.41]50100
    下载: 导出CSV

    表  2  单次MC平均运行时间

    Table  2  Average running time per MC

    方法时间(s)
    未互补估计(M1)2.7923
    视场互补估计(M2)9.8989
    共享估计(M3)32.7096
    下载: 导出CSV

    表  3  单次MC平均运行时间

    Table  3  Average running time per MC

    方法时间(s)
    单互补估计9.9252
    量测聚类估计10.4984
    未互补融合估计15.5495
    单共享估计31.5351
    互补融合估计45.5696
    下载: 导出CSV
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  • 收稿日期:  2020-06-29
  • 录用日期:  2021-01-15
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