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IMM-Singer模型的机动目标跟踪算法 被引量:18

Maneuvering Target Tracking Algorithm Based on IMM-Singer Model
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摘要 交互式多模型(IMM)算法是一种有效机动目标跟踪算法,但其性能与模型的选择、个数以及参数有关。Singer模型算法可以实现对机动目标的跟踪,但该算法存在机动频率和过程噪声大小等参数难以选取的问题。针对以上情况,利用IMM算法易于结合其他算法的特点,提出一种基于IMM-Singer模型的机动目标跟踪算法,实现Singer模型参数的自适应选择。仿真结果表明,该算法比单一的Singer模型算法或一般的IMM算法更能有效提高机动目标跟踪精度。 The Interacting Multiple Model(IMM) algorithm is an effective solution to maneuvering target tracking,however,its performance depends on the type,number and parameters of the models.The Singer Model algorithm can be utilized for maneuvering target tracking except that the parameters such as maneuver frequency,process noise and so on are hardly set.To the problems as mentioned above,with the merit that IMM can be combined with other algorithms easily,a novel filter of IMM-Singer Model is proposed such that the parameters of Singer Model can be set automatically.Simulation results show that compared with the single Singer Model and the general IMM,the proposed algorithm is more effective in improving the accuracy of maneuvering target tracking.
出处 《火力与指挥控制》 CSCD 北大核心 2012年第2期32-34,共3页 Fire Control & Command Control
基金 国家自然科学基金(60972159) 航空科学基金资助项目(20085184003)
关键词 交互式多模型 Singer模型 机动目标 interacting multiple model singer model maneuvering target
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参考文献7

  • 1BlomHAP,Bar-shalomY.TheInteractingMultiplemodelAlgorithmforSystemswithMarkovianSwitchingCoefficients[J].IEEETrans.onAutomaticControl,1988,AC-33(8):780-783.
  • 2MazorE,AverbuchAY,ShalomB.InteractingMultipleModelMethodsinTargetTracking:ASurvey[J].IEEETrans.onAerospaceandElectronicSystems,1998,34(1):103-122.
  • 3Byung-DooK,Ja-SungL.IMMalgorithmBasedontheAnalyticSolutionofSteadyStateKalmanFilterforRadarTargetTracking[C] //2005IEEEInternationalRadarConference,Arlington,Virginia,USA,2005:757-762.
  • 4廖永汉,朱胜利,彭冬亮.基于IMM滤波器的纯方位机动目标跟踪[J].火力与指挥控制,2010,35(1):20-23. 被引量:3
  • 5LiXR,JilkovVP.SurveyofManeuveringTargetTracking.part5:MultipleModelMethods[J].IEEERansactionsonAerospaceandElectronicSystems,2005,41(4):112-115.
  • 6SingerRA.EstimatingOptimalFilterTrackingPerformanceforMannedManeuveringTargets[J].IEEETransactionsonAerospaceandElectronicSystems,1970,6:473-483.
  • 7FitzgeraldRJ.SimpleTrackingFilters:SteadystateFilteringandSmoothingPerformance[J].IEEETransactionsonAerospaceandElectronicSystems,1980,16:860-864.

二级参考文献9

  • 1辛云宏,杨万海.基于伪线性卡尔曼滤波的多站IRST系统跟踪技术[J].红外与毫米波学报,2005,24(5):374-377. 被引量:15
  • 2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
  • 3Song T L, Speyer J. A Stochastic Analysis of a Modified Gain Extended Kalman Filter with Applications to Estimation with Bearings Only Measurements [J]. IEEE Trans. on Automatic Control, 1985, AC-30(10): 940-949.
  • 4Galkowski P,Islam M. An Alternative Derivation of the Modified Gain Function of Song and Speyer[J]. IEEE Trans. on Automatic Control, 1991, AC-36 (11):1322-1326.
  • 5Koteswara S R. Modified Gain Extended Kalman Filter with Application to Bearings-only Passive Manoeuvering Target Tracking [J]. IEEE Proc.- Radar Sonar Navig, 2005,152 (4) :239-244.
  • 6Blom H A, Bar-Shalom Y. The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients[J]. IEEE Trans. on Automatic Control, 1988, 33(8) : 780-783.
  • 7Li X R, Bar-Shalom Y. Prediction of Interacting Multiple Model Algorithm [J]. IEEE Trans on Aerospace and Electronic Systems, 1993, 29 (3) : 755-771.
  • 8Koteswara S R. Pseudo-linear Estimator for Bearing-only Passive Tracking, Radar Sonar and Navigation[J]. IEEE Proceedings, 2001,148:16-22.
  • 9Doucet A. On Sequential Monte Carlo Methods for Bayesian Filtering [D]. University of Cambridge, UK, Department of Engineering,1998.

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