空中飞行目标轨迹预测技术研究
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摘要
目标跟踪和轨迹预测技术在国防科研以及雷达、声呐信号处理及其他相关领域中是一个非常重要的研究课题,尤其是在当卡尔曼滤波理论提出并且广泛应用以后,目标跟踪技术更是发生了很大的进步,其中很多成果已经应用于侦察与预警、弹道导弹防御、交通管制等军事和民用领域当中,本论文着重研究了非线性卡尔曼滤波在目标跟踪预测中的应用。
文章首先对目标跟踪的基本原理进行了系统介绍,阐述了几种常用的目标跟踪模型、量测模型的选择和建立,然后介绍了常应用于线性系统中的卡尔曼滤波方法,根据实际的飞行体目标的要求,重点研究了扩展卡尔曼滤波算法(EKF)和粒子滤波算法(PF),针对扩展卡尔曼滤波算法在滤波过程中非线性系统的模型线性化处理过程中引入,因此不可避免地引入了线性化误差,采用粒子滤波进行处理取得较好的效果,其次本文采用非线性滤波建立了相应的目标跟踪预测模型,同时应用扩展卡尔曼滤波和粒子滤波对目标飞行体分别进行了预测滤波的仿真实验,通过仿真实验结果分析,表明了采用粒子滤波算法在目标飞行体跟踪预测方面有着更好的滤波跟踪性能。
最后文章应用了VC和Matlab混合编程的方式搭建了简易的预测系统的软件平台,也为今后目标跟踪预测提出了新的发展方向。
Target tracking and trajectory predicion technology is a very important research subject in defense research and radar, sonar signal processing and other related fields. A lot of progress has occurred in the area of target tracking technology and it has been used with wide especially after the development of the Kalman filter theory. Some examples of it have been used in reconnaissance and early warning systems for ballistic missile defense, Atraffic control, and other military and civilian areas. This thesis focuses on the use of the nonlinear Kalman filters in high-speed flight body tracking prediction.
Firstly, this paper introduces a detailed outline of the principle of target tracking as well as expounding on measurement model selection and development, and several common target tracking models.Then it introduces Kalman filter methods which are often applied to linear systems. According to the actual needs of high-speed flying bodies, this thesis focuses on the extended Kalman filter (EKF) and particle filter algorithm(PF). But the extended Kalman filter algorithm should first be linearized in the filter process of the nonlinear system, so it inevitably introduces linearization errors. However, the particle filter has advantages such as good results. This thesis uses nonlinear filtering to establish the relevant target tracking model. At the same time, it carries out a simulation experiment on high-speed flight bodies using both the extended Kalman filter and particle filter alternatively. Simulation analysis experiments showed the particle filter algorithm has better filter tracing performance in the tracking of high-speed flight bodies.
Finally, this paper builds a prediction system software platform using a mixture of VC and Matlab programming, revealing a new direction for target tracking prediction.
文章首先对目标跟踪的基本原理进行了系统介绍,阐述了几种常用的目标跟踪模型、量测模型的选择和建立,然后介绍了常应用于线性系统中的卡尔曼滤波方法,根据实际的飞行体目标的要求,重点研究了扩展卡尔曼滤波算法(EKF)和粒子滤波算法(PF),针对扩展卡尔曼滤波算法在滤波过程中非线性系统的模型线性化处理过程中引入,因此不可避免地引入了线性化误差,采用粒子滤波进行处理取得较好的效果,其次本文采用非线性滤波建立了相应的目标跟踪预测模型,同时应用扩展卡尔曼滤波和粒子滤波对目标飞行体分别进行了预测滤波的仿真实验,通过仿真实验结果分析,表明了采用粒子滤波算法在目标飞行体跟踪预测方面有着更好的滤波跟踪性能。
最后文章应用了VC和Matlab混合编程的方式搭建了简易的预测系统的软件平台,也为今后目标跟踪预测提出了新的发展方向。
Target tracking and trajectory predicion technology is a very important research subject in defense research and radar, sonar signal processing and other related fields. A lot of progress has occurred in the area of target tracking technology and it has been used with wide especially after the development of the Kalman filter theory. Some examples of it have been used in reconnaissance and early warning systems for ballistic missile defense, Atraffic control, and other military and civilian areas. This thesis focuses on the use of the nonlinear Kalman filters in high-speed flight body tracking prediction.
Firstly, this paper introduces a detailed outline of the principle of target tracking as well as expounding on measurement model selection and development, and several common target tracking models.Then it introduces Kalman filter methods which are often applied to linear systems. According to the actual needs of high-speed flying bodies, this thesis focuses on the extended Kalman filter (EKF) and particle filter algorithm(PF). But the extended Kalman filter algorithm should first be linearized in the filter process of the nonlinear system, so it inevitably introduces linearization errors. However, the particle filter has advantages such as good results. This thesis uses nonlinear filtering to establish the relevant target tracking model. At the same time, it carries out a simulation experiment on high-speed flight bodies using both the extended Kalman filter and particle filter alternatively. Simulation analysis experiments showed the particle filter algorithm has better filter tracing performance in the tracking of high-speed flight bodies.
Finally, this paper builds a prediction system software platform using a mixture of VC and Matlab programming, revealing a new direction for target tracking prediction.
引文
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[2]杨青智.机动目标跟踪技术的研究[D].西安：西安电子科技大学,2008.
[3]冯正.高速异常飞行体落点定位系统软件设计[D].太原：中北大学,2009.
[4]R.E. Kalman. A new approach to linear filtering and prediction problems. Journal of Basic engineering Trans. On ASME,1960,82 D(1):35.45
[5]李补莲.自动目标识别(ATR)技术发展述评[J],现代防御技术,2000,28(2)：10.20
[6]张毓晋,图像处理..图像工程(上册)(第二版)[M],北京：北京理工大学出版社,2006,45.260.
[7]R. S. Buey, K. D. Renne. Digital synthesis of Nonlinear Filter. Automatie, 1971,7(3):287.289.
[8]J. M. Hammersley, K. W. Morton, Poor man's Monte Carlo. Journal of the Royal Statistical Society B,1954,16(1):23.38.
[9]N.J. Gordon, D.J. Salmond, A.F.M. Smith. Novel approach to nonlinear/non.Gaussian Bayesian state estimation. IEE Proceedings F Radar and Signal processing,1993, 140(2):107.113.)
[10]M. Pitt, N. shephard. Filtering via simulation:Auxiliary particle filters. Journal of the American Statistical Association,1999,94(446):590.599.
[11]J.F.G. de freitas, M. Niranjan, A. H. Gee, A. Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation,2000,12(4):955.993.
[12]R. van der Merwe, A. Doucet, N. de. Freitas, E. Wan. The Unscented Particle Filter. Technical Report DUED/F.INFENG/TR 380, Engineering Department, Cambridge University, Aug.2000
[13]靳璐.机动目标跟踪及无迹滤波(UKF)的相关应用研究[D].太原：中北大学,2009.
[14]周宏仁、敬忠良、王培德.机动目标跟踪[M].北京：国防工业出版社,1991：(2.145).
[15]彭冬亮、文成林、薛安克.多传感器多源信息融合理论及应用[M].北京：科学出版社,2010：74.87
[16]M. Sanjeev Arulampalam, Simon Maskell,, Neil Gordon. A Tutorial on Particle for Online.Nonlinear/Non.Gaussian Bayesial Tracking. IEEE Trans. On AES, 2002,55(2):174.188
[17]何友、修建娟、张晶炜、关欣等.雷达数据处理及应用[M].北京：电子工业出版社,2006：42.49.
[18]A.Doucet, N.D.Freitas,N.Gordon. Sequential Monte Carlo Methods in Practice. Springer, 2001.
[19]A. Farina, B.Ristic. Tracking a Ballistic Target:Comparison of Several Nonlinear Filter. IEEE Trans.on AES,2002.38(3):425.437.
[20]权太范.目标跟踪新理论与技术[M].北京：国防工业出版社,2009：199.210.
[21]阳仁贵、潘小东.用VC++6.0和MATLAB开发测量数据处理系统[J].人民长江.2003,34.12：50.52.
[22]张丽军事运动目标的识别与跟踪研究[D].沈阳：东北大学,2009.
[23]何友、修建娟、张晶炜、关欣等[M].北京：电子工业出版社,2006：48.50.
[24]Shawn Michael Herman. A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification. PhD Thessis, University of Illinois,2002.
[25]王晓蓉.弹道修正火箭弹系统弹道卡尔曼滤波研究[D].太原：中北大学,2009.
[26]A.farina, B.Ristic. Tracking a Ballistic Target:Comparison of Several Nonlinear Filters.IEEE Trans.AES,2002,38(3):477-482
[27]J.S.Liu,R.Chen. Sequential Monte Carlo Methods for Dynamical Systems. Journal of the American Statistical Association,Vol93,1998:1032-1044.
[28]G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics,1996,5(1):1-25.
[29]Gordon, N.J., Salmond. Novel Approach to Nonlinear/Non-Gaussian Baysian State Estimation.IEEProceedings-Radarand
Signal Processing,Vol.140,No.2,1993,140(2):107-113
[30]H.Carvalho, P.Del Moral. Optimal Nonlinear Filtering in GPS/INS Intergation. IEEE Trans on AES,1997,33(3):835-849.
[31]Neil Gordon. A Hybrid Particle Filter for Target Tracking in Clutter. IEEE Trans. On AES, Vol.33,No.1,1997:353-358.
[32]M.Pitt, N.Shephard.Filtering Via Simulation:Auxiliary Pilters,Journal of the American Statistical Association,1999,94(446):590-599.
[33]James Carpenter, Peter Clifford, Paul Fearnhead. An Improved Particle Filter of Non-linear Problems. IEE proceedings-Radar,Sonar and Navigation,1996,146(l):2-7.
[34]Rudolph van der Merve, Arnaud Doucet. The Unscented Particle Filter,Technical Report CUED/FINFENG/TR 380,Cambrideg University,2000.
[35]Rickard Karlsson, Niclas Bergman. Auxiliary Particle Filters for Tracking a Maneuvering Target. Prceedings of the 39th IEEE Conference on Decision and Control,Vol.4,2000: 3891-2895
[36]A.Doucet, N. Gordon,V. Krishnamurthy.Particle Filters for State Estimation of Jump Markov Linear Systems.IEEE Trans.on Signal Processing,2001,49(3):613-624.
[37]Matthew Orton,Alan Marrs. Incorporation of Out-of-Sequence Measurement in Non-Linear Dynamic Systems Using Particle Filters. Technical Report,2001.
[38]Yukito Iba. Population Monte Carlo algorithms. Transactions of the Japanese Society for Artificial Intelligence,2001,16(2):279-286
[39]A.Doucet,N.D.Freitas,N.Gordon. Sequential Monte Carlo Methods in Practice. Springer, 2001.
[40]Shawn Michael Herman. A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification. PhD Thessis,University of Illinois,2002
[41]A.Farina, B.Ristic. Tracking a Ballistic Target:Comparison of Seeral Nonlinear Filters.IEEE Trans. on AES,2002,38(3):477-482.
[42]Richard Karlsson. Simulation Bassed Methods for Target Tracking.PhD Thesis, Linkoping University,2002.
[43]Fredrik Gustafsson, Fredrik Gunnarsson, Niclas Bergman, Urban Forssell. Particle Filters for Positioning,Navigation and Trackin. IEEE Trans. On SP,2002,50(2):425-437.
[44]赵瑞、顾启泰.滤波理论的最新进展及其在导航系统中的应用.清华大学学报,2000,40(5)：24-27.
[45]邓自立.最优估计理论及其应用.哈尔滨：哈尔滨工业大学出版社,2005：(98-114)
[46]彭竞.非线性滤波技术在卫星导航系统中的应用研究.北京：国防科学技术大学研究生院.2005.
[47]许江湖,张永胜.机动目标建模技术概述.[J]现代雷达.2002,5：(10-15).
[48]彭云辉,刘云峰.目标跟踪中两种非线性滤波算法的对比研究.[J].2008.5.3：(57-59).
[49]胡士强,敬忠良.粒子滤波算法综述.控制与决策[J].2005,20.4：(361-363).
[50]赵延.机动目标自适应卡尔曼滤波算法研究.成都：电子科技大学.2004.
[51]姚剑敏.粒子滤波跟踪方法研究[D].中科院博士论文,2004.
[2]杨青智.机动目标跟踪技术的研究[D].西安：西安电子科技大学,2008.
[3]冯正.高速异常飞行体落点定位系统软件设计[D].太原：中北大学,2009.
[4]R.E. Kalman. A new approach to linear filtering and prediction problems. Journal of Basic engineering Trans. On ASME,1960,82 D(1):35.45
[5]李补莲.自动目标识别(ATR)技术发展述评[J],现代防御技术,2000,28(2)：10.20
[6]张毓晋,图像处理..图像工程(上册)(第二版)[M],北京：北京理工大学出版社,2006,45.260.
[7]R. S. Buey, K. D. Renne. Digital synthesis of Nonlinear Filter. Automatie, 1971,7(3):287.289.
[8]J. M. Hammersley, K. W. Morton, Poor man's Monte Carlo. Journal of the Royal Statistical Society B,1954,16(1):23.38.
[9]N.J. Gordon, D.J. Salmond, A.F.M. Smith. Novel approach to nonlinear/non.Gaussian Bayesian state estimation. IEE Proceedings F Radar and Signal processing,1993, 140(2):107.113.)
[10]M. Pitt, N. shephard. Filtering via simulation:Auxiliary particle filters. Journal of the American Statistical Association,1999,94(446):590.599.
[11]J.F.G. de freitas, M. Niranjan, A. H. Gee, A. Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation,2000,12(4):955.993.
[12]R. van der Merwe, A. Doucet, N. de. Freitas, E. Wan. The Unscented Particle Filter. Technical Report DUED/F.INFENG/TR 380, Engineering Department, Cambridge University, Aug.2000
[13]靳璐.机动目标跟踪及无迹滤波(UKF)的相关应用研究[D].太原：中北大学,2009.
[14]周宏仁、敬忠良、王培德.机动目标跟踪[M].北京：国防工业出版社,1991：(2.145).
[15]彭冬亮、文成林、薛安克.多传感器多源信息融合理论及应用[M].北京：科学出版社,2010：74.87
[16]M. Sanjeev Arulampalam, Simon Maskell,, Neil Gordon. A Tutorial on Particle for Online.Nonlinear/Non.Gaussian Bayesial Tracking. IEEE Trans. On AES, 2002,55(2):174.188
[17]何友、修建娟、张晶炜、关欣等.雷达数据处理及应用[M].北京：电子工业出版社,2006：42.49.
[18]A.Doucet, N.D.Freitas,N.Gordon. Sequential Monte Carlo Methods in Practice. Springer, 2001.
[19]A. Farina, B.Ristic. Tracking a Ballistic Target:Comparison of Several Nonlinear Filter. IEEE Trans.on AES,2002.38(3):425.437.
[20]权太范.目标跟踪新理论与技术[M].北京：国防工业出版社,2009：199.210.
[21]阳仁贵、潘小东.用VC++6.0和MATLAB开发测量数据处理系统[J].人民长江.2003,34.12：50.52.
[22]张丽军事运动目标的识别与跟踪研究[D].沈阳：东北大学,2009.
[23]何友、修建娟、张晶炜、关欣等[M].北京：电子工业出版社,2006：48.50.
[24]Shawn Michael Herman. A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification. PhD Thessis, University of Illinois,2002.
[25]王晓蓉.弹道修正火箭弹系统弹道卡尔曼滤波研究[D].太原：中北大学,2009.
[26]A.farina, B.Ristic. Tracking a Ballistic Target:Comparison of Several Nonlinear Filters.IEEE Trans.AES,2002,38(3):477-482
[27]J.S.Liu,R.Chen. Sequential Monte Carlo Methods for Dynamical Systems. Journal of the American Statistical Association,Vol93,1998:1032-1044.
[28]G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics,1996,5(1):1-25.
[29]Gordon, N.J., Salmond. Novel Approach to Nonlinear/Non-Gaussian Baysian State Estimation.IEEProceedings-Radarand
Signal Processing,Vol.140,No.2,1993,140(2):107-113
[30]H.Carvalho, P.Del Moral. Optimal Nonlinear Filtering in GPS/INS Intergation. IEEE Trans on AES,1997,33(3):835-849.
[31]Neil Gordon. A Hybrid Particle Filter for Target Tracking in Clutter. IEEE Trans. On AES, Vol.33,No.1,1997:353-358.
[32]M.Pitt, N.Shephard.Filtering Via Simulation:Auxiliary Pilters,Journal of the American Statistical Association,1999,94(446):590-599.
[33]James Carpenter, Peter Clifford, Paul Fearnhead. An Improved Particle Filter of Non-linear Problems. IEE proceedings-Radar,Sonar and Navigation,1996,146(l):2-7.
[34]Rudolph van der Merve, Arnaud Doucet. The Unscented Particle Filter,Technical Report CUED/FINFENG/TR 380,Cambrideg University,2000.
[35]Rickard Karlsson, Niclas Bergman. Auxiliary Particle Filters for Tracking a Maneuvering Target. Prceedings of the 39th IEEE Conference on Decision and Control,Vol.4,2000: 3891-2895
[36]A.Doucet, N. Gordon,V. Krishnamurthy.Particle Filters for State Estimation of Jump Markov Linear Systems.IEEE Trans.on Signal Processing,2001,49(3):613-624.
[37]Matthew Orton,Alan Marrs. Incorporation of Out-of-Sequence Measurement in Non-Linear Dynamic Systems Using Particle Filters. Technical Report,2001.
[38]Yukito Iba. Population Monte Carlo algorithms. Transactions of the Japanese Society for Artificial Intelligence,2001,16(2):279-286
[39]A.Doucet,N.D.Freitas,N.Gordon. Sequential Monte Carlo Methods in Practice. Springer, 2001.
[40]Shawn Michael Herman. A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification. PhD Thessis,University of Illinois,2002
[41]A.Farina, B.Ristic. Tracking a Ballistic Target:Comparison of Seeral Nonlinear Filters.IEEE Trans. on AES,2002,38(3):477-482.
[42]Richard Karlsson. Simulation Bassed Methods for Target Tracking.PhD Thesis, Linkoping University,2002.
[43]Fredrik Gustafsson, Fredrik Gunnarsson, Niclas Bergman, Urban Forssell. Particle Filters for Positioning,Navigation and Trackin. IEEE Trans. On SP,2002,50(2):425-437.
[44]赵瑞、顾启泰.滤波理论的最新进展及其在导航系统中的应用.清华大学学报,2000,40(5)：24-27.
[45]邓自立.最优估计理论及其应用.哈尔滨：哈尔滨工业大学出版社,2005：(98-114)
[46]彭竞.非线性滤波技术在卫星导航系统中的应用研究.北京：国防科学技术大学研究生院.2005.
[47]许江湖,张永胜.机动目标建模技术概述.[J]现代雷达.2002,5：(10-15).
[48]彭云辉,刘云峰.目标跟踪中两种非线性滤波算法的对比研究.[J].2008.5.3：(57-59).
[49]胡士强,敬忠良.粒子滤波算法综述.控制与决策[J].2005,20.4：(361-363).
[50]赵延.机动目标自适应卡尔曼滤波算法研究.成都：电子科技大学.2004.
[51]姚剑敏.粒子滤波跟踪方法研究[D].中科院博士论文,2004.