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联合约束级联交互式多模型滤波器及其在机动目标跟踪中的应用

夏小虎 刘明

夏小虎, 刘明. 联合约束级联交互式多模型滤波器及其在机动目标跟踪中的应用[J]. 电子与信息学报, 2017, 39(1): 117-123. doi: 10.11999/JEIT160384
引用本文: 夏小虎, 刘明. 联合约束级联交互式多模型滤波器及其在机动目标跟踪中的应用[J]. 电子与信息学报, 2017, 39(1): 117-123. doi: 10.11999/JEIT160384
XIA Xiaohu, LIU Ming. Unified Constrained Cascade Interactive Multi-model Filter and Its Application in Tracking of Manoeuvring Target[J]. Journal of Electronics & Information Technology, 2017, 39(1): 117-123. doi: 10.11999/JEIT160384
Citation: XIA Xiaohu, LIU Ming. Unified Constrained Cascade Interactive Multi-model Filter and Its Application in Tracking of Manoeuvring Target[J]. Journal of Electronics & Information Technology, 2017, 39(1): 117-123. doi: 10.11999/JEIT160384

联合约束级联交互式多模型滤波器及其在机动目标跟踪中的应用

doi: 10.11999/JEIT160384
基金项目: 

国家自然科学基金(61340016),安徽省自然科学基金(1408085MF134),安徽省高校优秀青年骨干人才国内外访学研修重点项目(gxfxZD2016224)

Unified Constrained Cascade Interactive Multi-model Filter and Its Application in Tracking of Manoeuvring Target

Funds: 

The National Natural Science Fundation of China (61340016), Anhui Province Natural Science Foundation (1408085MF134), Anhui Province Youth Leading Talents and Visiting Scholar Key Scheme (gxfxZD2016224)

  • 摘要: 该文提出一种新型联合约束的级联交互式多模型卡尔曼滤波器,该滤波器由两个滤波器前后两级串联而成;第1级为标准交互式多模型滤波器;第2级为联合约束滤波器。联合约束滤波器的约束条件对第1级滤波器中的多模型集合中各子模型均有效。联合约束滤波器采用平滑约束卡尔曼滤波算法对第1级滤波结果进一步优化。以机动目标实时跟踪为实际工程应用背景,数值仿真和飞行实验结果证明新的联合约束性级联交互式多模型滤波器比标准交互式多模型滤波器具有更小的估计误差和方差,所增计算量合理可行。该文为交互式多模型滤波器和机动目标跟踪两个方向的进一步改进提供了有益借鉴。
  • YANG Jinlong, JI Hongling, and FAN Zhenhua. Probability hypothesis density filter based on strong tracking MIE for multiple maneuvering targets tracking[J]. International Journal of Control, Automation and Systems, 2013, 11(2): 306-316.
    LI Bo. Multiple-model Rao-Blackwellized particle CPHD filter for multi-target tracking[J]. Nonlinear Dynamics, 2015, 79(3): 2133-2143.
    LIU Meiqin, ZHANG Di, and ZHANG Senlin. Bearing-only target tracking using cubature rauch-tung-striebel smoother [C]. 34th Chinese Control Conference, Hangzhou, China, 2015: 4734-4738.
    EVERS C, MOORE A H, NAYLOR P A, et al. Bearing- only acoustic tracking of moving speakers for robot audition [C]. IEEE International Conference on Digital Signal Processing, Singapore, 2015: 1206-1210.
    BECKER S, MUNCH D, KIERITZ H, et al. Detecting abandoned objects using interacting multiple models[J]. SPIE, 2015, 96520. doi: 10.1117/12.2195224.
    SABORDO M G and ABOUTANIOS E. Enhanced performance for the interacting multiple model estimator with integrated multiple filters[J]. SPIE, 2015, 94600: 345-349. doi: 10.1117/12.2176180.
    MABROUK M B, GRIVEL E, MAGNANT C, et al. Compensating power amplifier distortion in cognitive radio systems with adaptive interacting multiple model[C]. 23rd European Signal Processing Conference (EUSIPCO), Nice, France, 2015: 1212-1216.
    HUANG H, YANG R, NG G W, et al. Helicopter tracking and classification with multiple interacting multiple model estimator with out-of-sequence acoustic and EO measurements[C]. 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, 2016: 1132-1139.
    KIM T H and MOON K R. Variable-structured interacting multiple model algorithm for the ballistic coefficient estimation of a re-entry ballistic target[J]. International Journal of Control, Automation, and systems, 2013, 11(6): 1204-1213.
    ZHU Zhengwei. Ship-borne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model[J]. Journal of Zhejiang University SCIENCE C, 2013, 14(9): 733-742.
    ZHANG Yuan, GUO Chen, HU Hai, et al. An algorithm of the adaptive grid and fuzzy interacting multiple models[J]. Journal of Marine Science and Application, 2014, 13(3): 340-345.
    DAN S and TIEN L C. Kalman filtering with state equality constraints[J]. IET Control Theory and Applications, 2010, 4(8): 1303-1318.
    HARTIKAINEN J, SOLIN A, and SARKKA S. Optimal filtering with kalman filters and smoothers a manual for the MATLAB toolbox EKF/UKF[R]. 2011.
    盛骤, 等. 概率论与数理统计[M]. 北京: 高等教育出版社, 1989: 73-75.
    SHENG Zhou, et al. Probability and Statistics[M]. Beijing: High, Education Press, 1989: 73-75.
    CHIA T. Parameter identification and state estimation of constrained systems[D]. [Ph.D. dissertation], Case Western Reserve University, 1985, 17-25.
    KO S and BITMEAD R. State estimation for linear systems with state equality constraints[J]. Automatica, 2007, 43(8): 1363-1368.
    SHIMADA N, SHIRAI Y, KUNO Y, et al. Hand gesture estimation and model refinement using monocular camera- ambiguity limitation by inequality constraints[C]. IEEE International Conference on Automatic Face Gesture Recognition, Japan, 1998: 268-273.
    SIMON D and SIMON D L. Constrained Kalman filtering via density function truncation for turbofan engine health estimation[J]. International Journal of System Science, 2010, 41(2): 159-171.
    DE GEETER J, VAN BRUSSEL H, and DE SCHUTTER J. A smoothly constrained Kalman filter[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 1997, 19(10): 1171-1177.
    GOODWIN G, SERON M, and DE DONA J. Constrained control and estimation[M]. Berlin: Springer-Verlag, 2005: 251-262.
    JULIER S and UHLMANN J. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422.
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出版历程
  • 收稿日期:  2016-04-20
  • 修回日期:  2016-12-06
  • 刊出日期:  2017-01-19

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