基于DP-TBD的分布式异步迭代滤波融合算法研究

李洋漾 李雯 易伟 孔令讲

李洋漾, 李雯, 易伟, 孔令讲. 基于DP-TBD的分布式异步迭代滤波融合算法研究[J]. 雷达学报, 2018, 7(2): 254-262. doi: 10.12000/JR17057
引用本文: 李洋漾, 李雯, 易伟, 孔令讲. 基于DP-TBD的分布式异步迭代滤波融合算法研究[J]. 雷达学报, 2018, 7(2): 254-262. doi: 10.12000/JR17057
Li Yangyang, Li Wen, Yi Wei, Kong Lingjiang. A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD[J]. Journal of Radars, 2018, 7(2): 254-262. doi: 10.12000/JR17057
Citation: Li Yangyang, Li Wen, Yi Wei, Kong Lingjiang. A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD[J]. Journal of Radars, 2018, 7(2): 254-262. doi: 10.12000/JR17057

基于DP-TBD的分布式异步迭代滤波融合算法研究

doi: 10.12000/JR17057
基金项目: 长江学者奖励计划,中央高校基本科研基金(ZYGX2016J031),中国博士后科学基金面上基金(2014M550465)和特别资助基金(2016T90845)
详细信息
    作者简介:

    李洋漾(1993–),男,四川人,电子科技大学硕士研究生,研究方向为多传感器数据融合理论、弱小目标检测跟踪技术。E-mail: 575630861@qq.com

    李雯:李 雯(1993–),女,陕西人,电子科技大学硕士研究生,研究方向为雷达信号处理、雷达通信一体化波形设计。E-mail: 1757152507@qq.com

    易伟:易 伟(1983–),男,四川人,电子科技大学副教授,研究方向为统计信号处理、雷达信号与数据处理、多传感器数据融合理论、弱小目标检测跟踪技术等。E-mail: kussoyi@gmail.com

    孔令讲(1974–),男,河南人,博士,电子科技大学教授,研究方向为宽带雷达系统技术、弱目标检测跟踪技术、雷达协同探测技术、相控阵激光雷达技术,科研概况:主要承担国家863、国防预研、自然基金等科研项目

    通讯作者:

    李洋漾 575630861@qq.com

A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD

Funds: The Chang Jiang Scholars Program, The Fundamental Research Funds of Central Universities under Grants (ZYGX2016J031), The Chinese Postdoctoral Science Foundation under Grant (2014M550465) and Special Grant (2016T90845)
  • 摘要: 该文主要运用检测前跟踪动态规划(Dynamic Programming-Track Before Detect)算法解决目标跟踪问题。动态规划(Dynamic Programming, DP)是一种通过对量测空间栅格化处理,然后对离散的量测空间中所有可能的物理路径进行遍历的算法。然而,该算法提供的是一种未经滤波和平滑的点迹序列。随着实际战争环境日益复杂,基于单雷达的DP-TBD算法在信噪比(SNR)较低时跟踪效果不佳。此外,由于DP-TBD算法没有状态误差协方差矩阵,因此无法将不同雷达的点迹序列进行融合。而且由于通信时延和不同的采样周期,不同雷达的数据往往是异步的。为了解决以上问题,该文提出了一种基于DP-TBD的分布式异步迭代滤波融合算法(DynamicProgramming Fuison, DPF)。该算法分为两步,第1步提出了一种迭代滤波方法对DP点迹进行处理;第2步将不同雷达获得的异步状态估计转化为同步的,接着利用几种分布式的融合方法来获取融合之后的状态估计。仿真结果说明,和单雷达相比,该融合算法可以有效提升目标跟踪的性能,同时,该算法也可以降低航迹丢失率和计算量。

     

  • 图  1  N=5时滑窗间多帧关系示意图

    Figure  1.  The relationships of multiple sliding window frames when N=5

    图  2  DPF算法详细流程图

    Figure  2.  The detail method flow of DPF algorithm

    图  3  异步数据转换示意图

    Figure  3.  Asynchronous data conversion figure

    图  4  当SNR=8时的一次蒙特卡洛仿真结果

    Figure  4.  One time Monte Carlo simulation result when SNR=8

    图  5  从第1帧到第20帧的RMSE

    Figure  5.  RMSE from the 1st frame to the 20nd frame

    图  6  信噪比从8 dB到16 dB, DPF算法和TDF算法的航迹丢失率

    Figure  6.  Track loss rate of DPF algorithm and TDF method from SNR=8 dB to SNR=16 dB

    表  1  DPF算法和CDPF算法一次DP迭代的执行时间(s)

    Table  1.   One time execution time of DPF algorithm and CDPF algorithm (s)

    参数 DPF算法 CDPF算法
    传感器1的跟踪时间 4.07 0.052
    传感器2的跟踪时间 3.661 0.043
    融合时间 0.1 7.74
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  • [1] Bar-Shalom Y and Li Xiao-rong. Multitarget-Multisensor Tracking: Principles and Techniques[M]. Storrs, CT: YBS, 1995.
    [2] Bar-Shalom Y and Blair W D. Multitarget-Multisensor Tracking: Applications and Advances, Vol. III[M]. Norwood, MA: Artech House, 2000.
    [3] Bar-Shalom Y, Daum F, and Huang J. The probabilistic data association filter[J]. IEEE Control Systems, 2009, 29(6): 82–100. DOI: 10.1109/MCS.2009.934469
    [4] Davey S J and Rutten M G. A comparison of three algorithms for tracking dim targets[C]. Proceedings of 2007 IEEE Information, Decision and Control, Adelaide, Australia, 2007: 342–347.
    [5] Barniv Y. Dynamic programming solution for detecting dim moving targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 1985, AES-21(1): 144–156. DOI: 10.1109/TAES.1985.310548
    [6] Barniv Y and Kella O. Dynamic programming solution for detecting dim moving targets part II: Analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 1987, AES-23(6): 776–788. DOI: 10.1109/TAES.1987.310914
    [7] Arnold J, Shaw S W, and Pasternack H. Efficient target tracking using dynamic programming[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 44–56. DOI: 10.1109/7.249112
    [8] Tonissen S M and Evans R J. Peformance of dynamic programming techniques for Track-Before-Detect[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(4): 1440–1451. DOI: 10.1109/7.543865
    [9] Johnston L A and Krishnamurthy V. Performance analysis of a dynamic programming track before detect algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(1): 228–242. DOI: 10.1109/7.993242
    [10] Yi Wei, Morelande M R, Kong Ling-jiang, et al. An efficient multi-frame track-before-detect algorithm for multi-target tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 421–434. DOI: 10.1109/JSTSP.2013.2256415
    [11] Buzzi S, Lops M, Venturino L, et al. Track-before-detect procedures in a multi-target environment[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1135–1150. DOI: 10.1109/TAES.2008.4655369
    [12] Wallace W R. The use of track-before-detect in pulse-Doppler radar[C]. Proceedings of RADAR 2002, Edinburgh, UK, 2002: 315–319.
    [13] Buzzi S, Lops M, and Venturino L. Track-before-detect procedures for early detection of moving target from airborne radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 937–954. DOI: 10.1109/TAES.2005.1541440
    [14] Orlando D, Ricci G, and Bar-Shalom Y. Track-before-detect algorithms for targets with Kinematic constraints[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 1837–1849. DOI: 10.1109/TAES.2011.5937268
    [15] Grossi E, Lops M, and Venturino L. A Novel dynamic programming algorithm for Track-Before-Detect in radar systems[J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2608–2619. DOI: 10.1109/TSP.2013.2251338
    [16] Yi Wei, Kong Ling-jiang, and Yang Jian-yu. Thresholding process based dynamic programming Track-before-detect algorithm[J]. IEICE Transactions on Communications, 2013, E96.B(1): 291–300. DOI: 10.1587/transcom.E96.B.291
    [17] Liu Rui, Yi Wei, Kong Ling-jiang, et al.. Recursive filtering for target tracking in multi-frame Track-Before-Detect[C]. Proceedings of the 2014 17th International Conference on Information Fusion (FUSION), Salamanca, Spain, 2014: 1–6.
    [18] Fang Zi-cheng, Yi Wei, and Kong Ling-jiang. A tracking approach for low observable target using plot-sequences of multi-frame detection[C]. Proceedings of the 2016 19th International Conference on Information Fusion, Heidelberg, Germany, 2016: 1427–1433.
    [19] Govaers F, Rong Yang, Chee L H, et al. Track-before-detect in distributed sensor applications[J]. EURASIP Journal on Advances in Signal Processing, 2011, 2011: 20. DOI: 10.1186/1687-6180-2011-20
    [20] Gao Xin-bo, Chen Jin-guang, Tao Da-cheng, et al. Multi-sensor centralized fusion without measurement noise covariance by variational Bayesian approximation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 718–727. DOI: 10.1109/TAES.2011.5705702
    [21] Ma Jing and Sun Shu-li. Centralized fusion estimators for multisensor systems with random sensor delays, multiple packet dropouts and uncertain observations[J]. IEEE Sensors Journal, 2013, 13(4): 1228–1235. DOI: 10.1109/JSEN.2012.2227995
    [22] Zhai Yan, Yeary M B, Havlicek J P, et al. A new centralized sensor fusion-tracking methodology based on particle filtering for power-aware systems[J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(10): 2377–2387. DOI: 10.1109/TIM.2008.919009
    [23] Mohammadi A and Asif A. Distributed particle filter implementation with intermittent/irregular consensus convergence[J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2572–2587. DOI: 10.1109/TSP.2013.2245123
    [24] Zhao Tong and Nehorai A. Distributed sequential Bayesian estimation of a diffusive source in wireless sensor networks[J]. IEEE Transactions on Signal Processing, 2007, 55(4): 1511–1524. DOI: 10.1109/TSP.2006.889975
    [25] Üney M, Clark D E, and Julier S J. Distributed fusion of PHD filters via exponential mixture densities[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 521–531. DOI: 10.1109/JSTSP.2013.2257162
    [26] Guo Yun-fei, Zeng Ze-bing, and Zhao Shang-yu. An amplitude association dynamic programming TBD algorithm with multistatic radar[C]. Proceedings of the 2016 35th Chinese Control Conference, Chengdu, China, 2016: 5076–5079.
    [27] Talebi H and Hemmatyar A. Asynchronous track-to-track fusion by direct estimation of time of sample in sensor networks[J]. IEEE Sensors Journal, 2014, 14(1): 210–217. DOI: 10.1109/JSEN.2013.2281394
    [28] Hu Yanyan, Duan Zhansheng, and Zhou Donghua. Estimation fusion with general asynchronous multi-rate sensors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 2090–2102. DOI: 10.1109/TAES.2010.5595618
    [29] Yan L P, Liu B S, and Zhou D H. Asynchronous multirate multisensor information fusion algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(3): 1135–1146. DOI: 10.1109/TAES.2007.4383603
    [30] Wang Yi-min and Li X R. Distributed estimation fusion with unavailable cross-correlation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 259–278. DOI: 10.1109/TAES.2012.6129634
    [31] Julier S J. An empirical study into the use of chernoff information for robust, distributed fusion of Gaussian mixture models[C]. Proceedings of the 2006 9th IEEE International Conference on Information Fusion, Florence, Italy, 2006: 1–8.
    [32] Aprile A, Grossi E, Lops M, et al.. An application of Track-Before-Detect to sea-clutter rejection: Experimental results based on real data[C]. Proceedings of the 2014 11th European Radar Conference, Rome, Italy, 2014: 505–508.
    [33] Li Yang-yang, Wang Jing-he, Yi Wei, et al.. A centralized asynchronous fusion algorithm for sensors with different resolution via DP-TBD[C]. Proceedings of 2017 IEEE Radar Conference, Seattle, USA, 2017: 922–927.
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出版历程
  • 收稿日期:  2017-06-14
  • 修回日期:  2017-07-31
  • 刊出日期:  2018-04-28

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