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基于D-S理论的分布交互式多传感器联合概率数据互联算法 被引量:6

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摘要 为了解决杂波环境下利用分布式多传感器系统跟踪多机动目标的问题,提出了一种分布交互式多传感器联合概率数据互联算法.该算法对每个传感器应用交互式联合概率数据互联法滤波,并将模型概率、状态估计等滤波结果送至融合中心.融合中心首先对各目标进行航迹相关判别并应用D-S证据理论对不同传感器关于同一目标的各模型概率进行融合,然后依此模型概率计算各目标状态估计并反馈至各传感器.最后给出了该算法的分析,仿真结果表明本算法能够很好地解决杂波环境下多传感器多机动目标的跟踪问题.
出处 《中国科学(E辑)》 CSCD 北大核心 2006年第2期182-190,共9页 Science in China(Series E)
基金 国家自然科学基金(批准号:60172033) 全国优秀博士论文作者专项资金(200036) 高校骨干教师资金(3240)资助项目
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参考文献10

  • 1Pao L Y,Frei C W.A comparison of parallel and sequential implementation of a multisensor multitarget tracking algorithm.In:Proc 1995 American Control Conf Seattie,1995.1683-1687
  • 2Chang K,Chong C,Bar S.Joint probabilistic data association in distributed sensor networks.IEEE Trans on AC,1986,31(10):889-897
  • 3Deb S.An S-dimentional assignment algorithm for track initiation.In:Proc of the IEEE Int Conf Systems Engineering,1992:527-530
  • 4Deb S.A multisensor-multitarget data association algorithm for heterogeneous sensors.IEEE Trans on AES,1993,29(2):560-568
  • 5Kirubarjan T,Bar S Y.Multiassignment for tracking a large number of overlapping objects.IEEE Trans on AES,2001,37(1):2-19
  • 6Blom H A P,Bloem E A.Interacting multiple track coalescence.In:Proc IEEE Conference on Decision and control,2002.3408-3415
  • 7Jilkov V P,Angelva D S.Design and comparison of model-set adaptive IMM algorithm for maneuvering target tracking.IEEE Trans on AES,1999,35(1):343-349
  • 8Kirubarjan T,Bar S Y.Ground target tracking with variable structure IMM estimator.IEEE Trans on AES,2000,36(6):26-46
  • 9Lin X D,Kirubarajan T,Bar S Y,et al.Enhanced accuracy GPS navigation using the IMM estimator.In:Proc 2001 IEEE Aerospace Conf,2001.1-15
  • 10Johnston L A,Krishnamurthy V.An improvement to the interacting multiple model (IMM) algorithm.IEEE Trans on Signal Processing,2001.2909-2923

同被引文献57

  • 1党玲,许江湖,王永斌.自适应网格交互多模型算法[J].火力与指挥控制,2004,29(4):51-54. 被引量:9
  • 2张晶炜,熊伟,何友.集中交互式多传感器联合概率数据互联算法[J].光电工程,2006,33(11):26-30. 被引量:7
  • 3李辉,沈莹,张安,程琤.交互式多模型目标跟踪的研究现状及发展趋势[J].火力与指挥控制,2006,31(11):1-4. 被引量:26
  • 4De FEO M,GRAZIANO A,MIGLIOLI R,et al.IMMJPDAversus MHT and Kalman filter with NN correlation:performance comparison[J].IEE Proc-Radar Sonar Navig.,1997,144(2):49-56.
  • 5CHAN B,TUGNAIT J K.Tracking of multiple maneuveringtargets in clutter using IMM/JPDA filtering and fixed-lagsmoothing[J].Automatica,2001,37:239-249.
  • 6TUGNAIT J K.Tracking of multiple maneuvering targets inclutter using multiple sensors,IMM,and JPDA coupledfiltering[J].IEEE Transactions on Aerospace andElectronic Systems,2004,40(1):320-330.
  • 7PURANIK S,TUGNAIT J K.Tracking of multiplemaneuvering targets using multiscan JPDA and IMMfiltering[J].IEEE Transactions on Aerospace andElectronic Systems,2007,43(1):23-35.
  • 8LI X R,BAR-SHALOM Y.Design of an interactingmultiple model algorithm for tracking in air traffic controlsystems[J].IEEE Transactions on Control SystemsTechnology,1993,1(3):186-194.
  • 9LI X R,ZHANG Y M.Multiple-model estimation withvariable structure part V:likely-model set algorithm[J].IEEE Transactions on Aerospace and Electronic Systems,2000,36(2):448-466.
  • 10VAHABIAN V,SEDIGH K A,et al.Optimal design of the variable structure IMM tracking filters using genetic algorithms[J].Proceedings of the2004IEEE International Conference on Control Applications,2004,2(9):1485-1490.

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