基于多模型与滚动时域估计的机动目标跟踪算法
|
摘要
针对受限于已知物理约束的机动目标,提出了一种目标跟踪算法。针对机动目标的不同运动模式,采用多模型组合的方法进行了近似;针对目标的已知物理约束,采用滚动时域估计方法进行处理,并将其作为状态估计的先验信息来提高估计精度;最终通过设计多模型结构的状态估计演化方程、改进滚动时域估计的误差协方差矩阵更新公式,给出了一种多模型结构与滚动时域估计相结合的机动目标跟踪算法。仿真结果表明:该算法与自适应卡尔曼滤波(AKF)、交互式多模型(IMM)算法相比,可以对具有物理约束的机动目标进行更好的跟踪。
Aimed at the problems that the maneuvering targets are restricted by some known physical constraints,a multiple model(MM)method is adopted.In accordance with various motion behavior of maneuvering targets,a composed multi-model method is used to approximate.And in the light of the known physical constrains of targets,a moving horizon estimation(MHE)is used to process,and a priori information of state estimation is utilized to promote the precision of estimation.To incorporate the MM method into MHE framework,through an estimation evolution formula and the modified update formula for the estimation covariance matrix,a MM-MHE optimization and algorithm are finally nally presented for the tracking problem.The simulation result shows that compared with the adaptive Kalman filter lter(AKF)and the interacting multiple model(IMM)method,this algorithm can perform well a task of tracking maneuvering targets(especially for the physically constrained motion condition).
Aimed at the problems that the maneuvering targets are restricted by some known physical constraints,a multiple model(MM)method is adopted.In accordance with various motion behavior of maneuvering targets,a composed multi-model method is used to approximate.And in the light of the known physical constrains of targets,a moving horizon estimation(MHE)is used to process,and a priori information of state estimation is utilized to promote the precision of estimation.To incorporate the MM method into MHE framework,through an estimation evolution formula and the modified update formula for the estimation covariance matrix,a MM-MHE optimization and algorithm are finally nally presented for the tracking problem.The simulation result shows that compared with the adaptive Kalman filter lter(AKF)and the interacting multiple model(IMM)method,this algorithm can perform well a task of tracking maneuvering targets(especially for the physically constrained motion condition).
引文
[1]MURAT E,DEREK P A.Maneuvering Target Tracking with an Adaptive Kalman Filter[C]//IEEE Conference on Decision&Control.1998:737-742.
[2]WANG D,CHENG C,ZHAO K.Study on Algorithm of EFK in CA Model for Bearing-Only Target Tracking[C]//International Conference on System of Systems Engineering.2012:244-246.
[3]JULIER S J,UHLMANN J K.UnscentedFiltering and Nonlinear Estimation[J].Proceeding of the IEEE,2004,92(3):401-422.
[4]ANDRIEU C,DOUCET A.Particle Filtering for Partially Observed Gaussian State Space Model[J].Journal of Royal Statistical Society-Series B,2002,64(4):827-836.
[5]LI X R,JILKOV V P.Survey of Maneuvering Target Tracking-Part V:Multiple-Model Methods[C]//IEEE Transactions on Aerospace And Electronic Systems.2005:1255-1321.
[6]孙福明,吴秀清,祁凯.基于当前统计模型的自适应强跟踪算法[J].数据采集与处理,2008,23(2):191-195.SUN Fuming,WU Xiuqing,QI Kai.Adaptive Strong Tracking Algorithm Based on Current Statistical Model[J].Journal of Data Acquisition&Processing,2008,23(2):191-195.(in Chinese).
[7]郝利超,雷虎民.基于修正模型的自适应跟踪算法[J].上海航天,2010,27(6):38-41.HAO Lichao,LEI Humin.An Adaptive Tracking Algorithm Based on Modied Model[J].Aerospace Shanghai,2010,27(6):38-41.(in Chinese)
[8]巫春玲,韩崇昭.一种新的自适应机动目标跟踪算法[J].系统仿真学报,2010,22(9):2164-2167.WU Chunling,HAN Chongzhao.Adaptive Maneuvering Target Tracking Algorithm Based on Current Statistical Model[J].Journal of System Simulation,2010,22(9):2164-2167.(in Chinese)
[9]MAZOR E,AVERBICJ A,BAR-SHALOM Y,et al.InteractingMultiple Model Methods in Target Tracking:A Survey[J].IEEE Transactions on Aerospace&Electronic Systems,1998,34(1):103-123.
[10]王占磊,张建业,张鹏,等.一种改进的变结构交互多模型被动跟踪算法[J].空军工程大学学报:自然科学版,2011,12(4):18-22.WANG Zhanlei,ZHANG Jianye,ZHANG Peng,et al.An Improved Variable Structure Interacting Multiple Model Passive Tracking Algorithm[J].Journal of Air Force Engineering University:Natural Science Edition,2011,12(4):18-22.(in Chinese)
[11]陆新东,胡振涛,刘先省,等.基于IMM多传感器顺序粒子滤波跟踪机动目标算法[J].传感器与微系统,2012,31(4):130-132.LU Xindong,HU Zhentao,LIU Xianxing,et al.Maneuvering Target Tracking Algorithm Based on IMM Multi-Sensor Sequential Particle Filtering[J].Transducer and Microsystem Technologies,2012,31(4):130-132.(in Chinese)
[12]付斌,丁月宁,黄勇,等.导引模型在FTC自适应IMMUKF目标跟踪算法中的应用[J].电光与控制,2014,21(1):68-72.FU Bin,DING Yuening,HUANG Yong,et al.Application of Guidance Model in FTC Adaptive IMM-UKF Maneuvering Target Tracking Algorithm[J].Electronics Optics&Control,2014,21(1):68-72.(in Chinese)
[13]FOO P H,NG G W.Combining the Interacting Multiple Model Method with Particle Filters for Maneuvering Target Tracking[J].Iet Radar Sonar&Navigation,2011,5(3):234-255.
[14]FOO P H.Combining theInteracting Multiple Model Method with Particle Filters for Manoeuvring Target Tracking with a Multi Static Radar System[J].Iet Radar Sonar Navigation,2011,5(7):697-706.
[15]RAO C V,RAWLINGS J B,LEE J H.ConstrainedLinear State Estimation-A Moving Horizon Approach[J].Automatica,2001,37(1):1619-1628(10).
[16]赵海艳,陈虹.具有未知干扰输入系统的滚动时域估计[J].电机与控制学报,2007,11(2):178-182.ZHAO Haiyuan,CHENG Hong.Moving Horizon Estimation for Stochastic Systems with Unknown Inputs[J].Electric Machines and Control,2007,11(2):178-182.(in Chinese)
[17]付东莉,柴毅.基于滚动时域估计的带约束运动目标跟踪[J].计算机应用研究,2011,28(3):877-879.FU Dongli,CHAI Yi.Constrained Target Tracking Based on Moving Horizon Estimation[J].Application Research of Computers,2011,28(3):877-879.(in Chinese)
[18]ALESSANDRI A,BAGLIETTO M,BATTISTELLI G.Receding-Horizon Estimation for Discrete-Time Linear Systems[J].IEEE Transactions on Automatic Control,2003,48(3):473-478.
[2]WANG D,CHENG C,ZHAO K.Study on Algorithm of EFK in CA Model for Bearing-Only Target Tracking[C]//International Conference on System of Systems Engineering.2012:244-246.
[3]JULIER S J,UHLMANN J K.UnscentedFiltering and Nonlinear Estimation[J].Proceeding of the IEEE,2004,92(3):401-422.
[4]ANDRIEU C,DOUCET A.Particle Filtering for Partially Observed Gaussian State Space Model[J].Journal of Royal Statistical Society-Series B,2002,64(4):827-836.
[5]LI X R,JILKOV V P.Survey of Maneuvering Target Tracking-Part V:Multiple-Model Methods[C]//IEEE Transactions on Aerospace And Electronic Systems.2005:1255-1321.
[6]孙福明,吴秀清,祁凯.基于当前统计模型的自适应强跟踪算法[J].数据采集与处理,2008,23(2):191-195.SUN Fuming,WU Xiuqing,QI Kai.Adaptive Strong Tracking Algorithm Based on Current Statistical Model[J].Journal of Data Acquisition&Processing,2008,23(2):191-195.(in Chinese).
[7]郝利超,雷虎民.基于修正模型的自适应跟踪算法[J].上海航天,2010,27(6):38-41.HAO Lichao,LEI Humin.An Adaptive Tracking Algorithm Based on Modied Model[J].Aerospace Shanghai,2010,27(6):38-41.(in Chinese)
[8]巫春玲,韩崇昭.一种新的自适应机动目标跟踪算法[J].系统仿真学报,2010,22(9):2164-2167.WU Chunling,HAN Chongzhao.Adaptive Maneuvering Target Tracking Algorithm Based on Current Statistical Model[J].Journal of System Simulation,2010,22(9):2164-2167.(in Chinese)
[9]MAZOR E,AVERBICJ A,BAR-SHALOM Y,et al.InteractingMultiple Model Methods in Target Tracking:A Survey[J].IEEE Transactions on Aerospace&Electronic Systems,1998,34(1):103-123.
[10]王占磊,张建业,张鹏,等.一种改进的变结构交互多模型被动跟踪算法[J].空军工程大学学报:自然科学版,2011,12(4):18-22.WANG Zhanlei,ZHANG Jianye,ZHANG Peng,et al.An Improved Variable Structure Interacting Multiple Model Passive Tracking Algorithm[J].Journal of Air Force Engineering University:Natural Science Edition,2011,12(4):18-22.(in Chinese)
[11]陆新东,胡振涛,刘先省,等.基于IMM多传感器顺序粒子滤波跟踪机动目标算法[J].传感器与微系统,2012,31(4):130-132.LU Xindong,HU Zhentao,LIU Xianxing,et al.Maneuvering Target Tracking Algorithm Based on IMM Multi-Sensor Sequential Particle Filtering[J].Transducer and Microsystem Technologies,2012,31(4):130-132.(in Chinese)
[12]付斌,丁月宁,黄勇,等.导引模型在FTC自适应IMMUKF目标跟踪算法中的应用[J].电光与控制,2014,21(1):68-72.FU Bin,DING Yuening,HUANG Yong,et al.Application of Guidance Model in FTC Adaptive IMM-UKF Maneuvering Target Tracking Algorithm[J].Electronics Optics&Control,2014,21(1):68-72.(in Chinese)
[13]FOO P H,NG G W.Combining the Interacting Multiple Model Method with Particle Filters for Maneuvering Target Tracking[J].Iet Radar Sonar&Navigation,2011,5(3):234-255.
[14]FOO P H.Combining theInteracting Multiple Model Method with Particle Filters for Manoeuvring Target Tracking with a Multi Static Radar System[J].Iet Radar Sonar Navigation,2011,5(7):697-706.
[15]RAO C V,RAWLINGS J B,LEE J H.ConstrainedLinear State Estimation-A Moving Horizon Approach[J].Automatica,2001,37(1):1619-1628(10).
[16]赵海艳,陈虹.具有未知干扰输入系统的滚动时域估计[J].电机与控制学报,2007,11(2):178-182.ZHAO Haiyuan,CHENG Hong.Moving Horizon Estimation for Stochastic Systems with Unknown Inputs[J].Electric Machines and Control,2007,11(2):178-182.(in Chinese)
[17]付东莉,柴毅.基于滚动时域估计的带约束运动目标跟踪[J].计算机应用研究,2011,28(3):877-879.FU Dongli,CHAI Yi.Constrained Target Tracking Based on Moving Horizon Estimation[J].Application Research of Computers,2011,28(3):877-879.(in Chinese)
[18]ALESSANDRI A,BAGLIETTO M,BATTISTELLI G.Receding-Horizon Estimation for Discrete-Time Linear Systems[J].IEEE Transactions on Automatic Control,2003,48(3):473-478.