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信任自适应事件触发鲁棒扩展卡尔曼融合滤波的目标跟踪

朱洪波 金嘉慧

朱洪波, 金嘉慧. 信任自适应事件触发鲁棒扩展卡尔曼融合滤波的目标跟踪[J]. 电子与信息学报. doi: 10.11999/JEIT250103
引用本文: 朱洪波, 金嘉慧. 信任自适应事件触发鲁棒扩展卡尔曼融合滤波的目标跟踪[J]. 电子与信息学报. doi: 10.11999/JEIT250103
ZHU Hongbo, JIN Jiahui. Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250103
Citation: ZHU Hongbo, JIN Jiahui. Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250103

信任自适应事件触发鲁棒扩展卡尔曼融合滤波的目标跟踪

doi: 10.11999/JEIT250103 cstr: 32379.14.JEIT250103
基金项目: 安徽高校自然科学研究项目重大项目(2023AH040157),国家自然科学基金(62003001)
详细信息
    作者简介:

    朱洪波:男,副教授,研究方向为网络系统状态估计、控制与优化及其应用

    金嘉慧:女,硕士生,研究方向为无线传感器网络下的移动目标跟踪

    通讯作者:

    金嘉慧 773975431@qq.com

  • 中图分类号: TP11

Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking

Funds: The Natural Science Research Project of Anhui Educational Committee (2023AH040157), The National Natural Science Foundation of China (62003001)
  • 摘要: 在资源受限移动无线传感器网络(MWSN)下的目标跟踪问题中,考虑到目标运动建模与接收信号强度(RSS)的不确定性,该文提出一种信任自适应事件触发鲁棒扩展卡尔曼融合滤波方法。首先,设计一种信任自适应事件驱动锚点调度与信息交互机制,其能适应目标周围信任锚点的分布,动态调度接近期望数量的信任锚点及其与移动目标间的信息交互,旨在确保信任测量下减少系统的电能、计算和带宽资源消耗。同时,构建一种基于均值漂移的鲁棒扩展卡尔曼信任融合滤波算法,通过对均匀分布的过程噪声协方差和测量噪声协方差进行随机采样,补偿运动建模和接收信号强度量化的不确定性,并仅对信任节点测量进行自适应权重融合估计,以改善目标跟踪的稳定性、鲁棒性与精确性。仿真结果表明:所提方法在降低电能、计算及带宽资源消耗的同时,提高了移动目标的跟踪精度,并展现出了对不确定性和异常节点测量的鲁棒性。
  • 图  1  MWSN环境下的单目标跟踪系统

    图  2  不同锚点调度机制下的信任响应锚点数量

    图  3  不同过程噪声协方差及采样次数的目标跟踪结果

    图  4  不同期望信任响应锚数量的目标跟踪结果

    图  5  不同RSS量化比特位数及采样次数的目标跟踪结果

    1  信任自适应事件驱动锚点调度与信息传输机制

     1.移动节点通过单跳通信发送定位请求信号给周围锚点,定位请
     求信号包括自身信息及响应半径。
     2.锚点测量并量化请求信号的RSS值${y_k}$。
     3.当RSS测量值${y_k}$满足式(16)时,锚点被调度为响应锚。响应锚
     依据式(18)生成RSS预滤波值${\hat y_k}$。
     4.当RSS测量值${y_k}$同时满足式(17)时,响应锚将其RSS测量值发
     送给移动目标。
     5.移动目标汇总所有响应锚的RSS数据以及ID信息,并统计响应
     锚点的数量。
     6.根据式(9)将目标汇总的RSS数据转换成距离值。
    下载: 导出CSV

    2  信任自适应事件触发鲁棒扩展卡尔曼融合滤波

    1. 初始化滤波器参数${\hat {\boldsymbol x}_{0|0}},{P_{0|0}},{{\boldsymbol Q}_0},{R_0}$,期望信任的响应锚点数量${N_{\rm t}}$,起始响应半径${y_{{\mathrm{rad}}(0)}}$
     2. while
     3.  应用算法1
     4.  通过式(20)、式(21)计算目标的先验状态值和误差协方差
     5.  for $i = 1:{N_{\mathrm{r}}}$
     6.   for $l = 1:L$
     7.    通过式(22)、式(23)生成移动目标的多重采样状态估计
         及误差协方差
     8.    通过式(24)生成滤波增益,并通过式(25)计算非线性距
         离函数的雅可比矩阵
     9.   end for $l$
     10.   通过式(26)、式(27)计算移动目标的后验状态估计和协方
         差矩阵
     11. end for $i$
     12. 通过式(28)提取预信任集
     13. while ${\text{|| }}{m_{k + 1}} - {m_k}|{|_2} \le \varepsilon $
     14.    通过式(29)、式(30)确定基于均值漂移的信任集
     15.    由式(31)、式(32)计算目标的信任融合状态估计值和协
          方差矩阵
     16. end while${\text{|| }}{m_{k + 1}} - {m_k}|{|_2} \le \varepsilon $
     17.设置$k = k + 1$
     18.end while
    下载: 导出CSV

    表  1  数值仿真参数列表

    参数 数值大小
    监测区域的尺寸(cm) 2000×2000
    初始响应半径(cm) 200
    采样时间间隔(s) 1
    实际量化比特数(bits) 8
    观测噪声协方差 $ {{R}}_{\text{k}}\text{=}\text{0.003}\text{×}{h}\left({{x}}_{{k}}\text{,}{{x}}^{{i}}\right) $
    过程噪声协方差 $ {{Q}}_{{k}}\text{=}{{q}}_{{k}}\text{×}{\text{I}}_{\text{4×4}} $
    下载: 导出CSV

    表  2  各种事件触发机制的 TNoTRA和 ANoTRA

    不同事件
    触发机制
    固定响应
    半径
    ${N_{\mathrm{t}}} = 4$ ${N_{\mathrm{t}}} = 5$ ${N_{\mathrm{t}}} = 8$ ${N_{\mathrm{t}}} = 10$
    TNoTRA 1316 483 607 965 1199
    ANoTRA 10.9667 4.0250 5.0583 8.0417 9.9917
    下载: 导出CSV
  • [1] GHATAOURA D S, MITCHELL J E, and MATICH G E. Networking and application interface technology for wireless sensor network surveillance and monitoring[J]. IEEE Communications Magazine, 2011, 49(10): 90–97. doi: 10.1109/MCOM.2011.6035821.
    [2] FANG Zheng, KONG Lingji, CHEN Jiangfan, et al. A multi-node self-powered fault detection system by triboelectric-electromagnetic nanosensors for smart transportation[J]. Nano Energy, 2024, 128: 109882. doi: 10.1016/j.nanoen.2024.109882.
    [3] BHASKER B and MURALI S. An Energy-Efficient Cluster-based data aggregation for agriculture irrigation management system using wireless sensor networks[J]. Sustainable Energy Technologies and Assessments, 2024, 65: 103771. doi: 10.1016/j.seta.2024.103771.
    [4] CACCIUTTOLO C, ATENCIO E, KOMARIZADEHASL S, et al. Internet of Things long-range-wide-area-network-based wireless sensors network for underground mine monitoring: Planning an efficient, safe, and sustainable labor environment[J]. Sensors, 2024, 24(21): 6971. doi: 10.3390/s24216971.
    [5] ALJUMAH A, AHANGER T A, and ULLAH I. Internet of things-based secure architecture to automate industry[J]. Cluster Computing, 2024, 27(8): 11103–11118. doi: 10.1007/s10586-024-04499-z.
    [6] 孟令军, 王宏涛, 夏善红. WSN节点声测距TOA值频域估计方法[J]. 电子与信息学报, 2010, 32(4): 993–997. doi: 10.3724/SP.J.1146.2009.00127.

    MENG Lingjun, WANG Hongtao, and XIA Shanhong. TOA estimation method in frequency domain for acoustic ranging of WSN Node[J]. Journal of Electronics & Information Technology, 2010, 32(4): 993–997. doi: 10.3724/SP.J.1146.2009.00127.
    [7] QIN Gezhou, LI Ming, FANG Sayin, et al. Study of a grid-based regional localization method for damage sources during three-point bending tests of wood[J]. Construction and Building Materials, 2024, 419: 135348. doi: 10.1016/j.conbuildmat.2024.135348.
    [8] CAI Zhen, ZHANG Fanhang, TAN Yuan, et al. Integration of an IoT sensor with angle-of-arrival-based angle measurement in AGV navigation: A reliability study[J]. Journal of Industrial Information Integration, 2024, 42: 100707. doi: 10.1016/j.jii.2024.100707.
    [9] 鲜江峰, 马俊领, 吴华锋, 等. 多参数未知下水声传感网由粗到精的定位方法[J]. 控制与决策, 2025, 40(1): 38–47. doi: 10.13195/j.kzyjc.2024.0521.

    XIAN Jiangfeng, MA Junling, WU Huafeng, et al. Coarse-to-fine localization method for UASNs under unknown multi-marameters[J]. Control and Decision, 2025, 40(1): 38–47. doi: 10.13195/j.kzyjc.2024.0521.
    [10] GANTASSI R, MESSOUS S, MASOOD Z, et al. Enhanced network QoS in large scale and high sensor node density wireless sensor networks using (IR-DV-Hop) localization algorithm and Mobile Data Collector (MDC)[J]. IEEE Access, 2024, 12: 37957–37973. doi: 10.1109/ACCESS.2024.3370432.
    [11] REKHA and GARG R. K-LionER: Meta-heuristic approach for energy efficient cluster based routing for WSN-assisted IoT networks[J]. Cluster Computing, 2024, 27(4): 4207–4221. doi: 10.1007/s10586-024-04280-2.
    [12] TAGNE E F, KAMDJOU H M, AMRAOUI A E, et al. A lossless distributed data compression and aggregation methods for low resources wireless sensors platforms[J]. Wireless Personal Communications, 2023, 128(1): 621–643. doi: 10.1007/s11277-022-09970-x.
    [13] 高慧敏, 杨磊, 朱军龙, 等. 基于事件触发的通信有效联邦学习算法[J]. 电子与信息学报, 2023, 45(10): 3710–3718. doi: 10.11999/JEIT220131.

    GAO Huimin, YANG Lei, ZHU Junlong, et al. Communication-efficient federated learning algorithm based on event triggering[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3710–3718. doi: 10.11999/JEIT220131.
    [14] GE Xiaohua, HAN Qinglong, and WANG Zidong. A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks[J]. IEEE Transactions on Cybernetics, 2019, 49(1): 171–183. doi: 10.1109/TCYB.2017.2769722.
    [15] GE Xiaohua and HAN Qinglong. Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8118–8127. doi: 10.1109/TIE.2017.2701778.
    [16] ZOU Fangling and WU Kang. Practical prescribed time tracking control for a class of nonlinear systems with event triggering and output constraints[J]. International Journal of Robust and Nonlinear Control, 2024, 34(17): 11786–11803. doi: 10.1002/rnc.7612.
    [17] 王鼎, 胡凌治, 赵明明, 等. 未知非线性零和博弈最优跟踪的事件触发控制设计[J]. 自动化学报, 2023, 49(1): 91–101. doi: 10.16383/j.aas.c220378.

    WANG Ding, HU Lingzhi, ZHAO Mingming, et al. Event-triggered control design for optimal tracking of unknown nonlinear zero-sum games[J]. Acta Automatica Sinica, 2023, 49(1): 91–101. doi: 10.16383/j.aas.c220378.
    [18] BAI Shuo, HU Jingyu, YAN Yongjun, et al. An integrated approach for vehicle state estimation under non-ideal conditions using adaptive strong tracking maximum correntropy criterion EKF[J]. IEEE Transactions on Vehicular Technology, 2024, 73(10): 14604–14616. doi: 10.1109/TVT.2024.3399065.
    [19] KANG Jiarong, WANG Yi, and XIONG Xiaobin. Fast decentralized state estimation for legged robot locomotion via EKF and MHE[J]. IEEE Robotics and Automation Letters, 2024, 9(12): 10914–10921. doi: 10.1109/LRA.2024.3483043.
    [20] CHARKHGARD M and FARROKHI M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF[J]. IEEE Transactions on Industrial Electronics, 2010, 57(12): 4178–4187. doi: 10.1109/TIE.2010.2043035.
    [21] 周云, 胡锦楠, 赵瑜, 等. 基于卡尔曼滤波改进压缩感知算法的车辆目标跟踪[J]. 湖南大学学报(自然科学版), 2023, 50(1): 11–21. doi: 10.16339/j.cnki.hdxbzkb.2023002.

    ZHOU Yun, HU Jinnan, ZHAO Yu, et al. Vehicle target tracking based on kalman filtering improved compressed sensing algorithm[J]. Journal of Hunan University (Natural Sciences), 2023, 50(1): 11–21. doi: 10.16339/j.cnki.hdxbzkb.2023002.
    [22] YU Xingkai and MENG Ziyang. Robust Kalman filters with unknown covariance of multiplicative noise[J]. IEEE Transactions on Automatic Control, 2024, 69(2): 1171–1178. doi: 10.1109/TAC.2023.3277866.
    [23] WANG Yiting, FU Jingqi, and WU Zetai. A novel fusion estimation method for RSS-AOA-based indoor target tracking[J]. IEEE Sensors Journal, 2024, 24(14): 22632–22647. doi: 10.1109/JSEN.2024.3405546.
    [24] GUO Guangyi, CHEN Ruizhi, YAN Ke, et al. Multichannel and multi-RSS based BLE range estimation for indoor tracking of commercial smartphones[J]. IEEE Sensors Journal, 2023, 23(24): 30728–30738. doi: 10.1109/JSEN.2023.3328711.
    [25] GUO Guangyi, CHEN Ruizhi, NIU Xiaoguang, et al. Factor graph framework for smartphone indoor localization: Integrating data-driven PDR and Wi-Fi RTT/RSS ranging[J]. IEEE Sensors Journal, 2023, 23(11): 12346–12354. doi: 10.1109/JSEN.2023.3267121.
    [26] ZHU Hongbo, AMURI M J V, LI Xueyang, et al. Mean-shift-based outliers-robust distributed Kalman filter for wireless sensor network systems[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 9500312. doi: 10.1109/TIM.2024.3497172.
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出版历程
  • 收稿日期:  2025-02-21
  • 修回日期:  2025-05-25
  • 网络出版日期:  2025-06-07

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