基于Jerk模型的高机动目标跟踪算法研究
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摘要
不论在军事还是民用领域中,目标跟踪都有着广泛的应用,譬如空中防御和空中交通管制中,因此受到人们的广泛的关注,随着科学技术的不断发展各种飞行器的机动性能不断和速度也在不断的提高,因此,如何提高高速高机动目标的跟踪性能成为一个更加重要的问题。由于目标机动出现的随机性、复杂性和多样性,跟踪系统的性能也会随之变的恶化,对机动目标进行跟踪,在理论和实践上都有较高的技术难度。因此,本文在学习前人研究成果的基础上,着重研究了高机动目标的跟踪问题。
本文首先回顾了机动目标跟踪问题的发展历程和研究现状,详细介绍了目标跟踪的理论知识:状态估计、参数估计与状态估计、卡尔曼滤波等滤波方法。在此基础上,对相关的问题进行了研究,由于运动目标的高速高机动性,迫切要求我们能够更加准切的描述目标的模型,机动频率是描述运动目标机动性的参数,该参数一般是根据人们的经验认为设定的,因此,在对运动目标的跟踪过程中不可避免的引入人为误差,降低了跟踪精度。
本文主要研究的高机动高速运动目标的跟踪问题,开展了一下研究工作:
1、首先利用系统辨识中参数辨识的有关知识,结合Jerk模型提出了机动频率的辨识方法,同时由于观测噪声的存在,使得辨识的无偏性不能满足,因此对辨识算法提出了合理的补偿方法。实验结果表明,该方法能对目标运动模型中的机动频率进行良好的辨识。
2、根据系统辨识相关知识,参数也可以看作系统的一个状态,因此将机动频率作为系统状态方程中的一个状态进行建模,提出了α? CS ? Jerk模型,同时由于非线性的存在,结合不敏卡尔曼进行滤波处理,通过仿真表明,该模型和相应的滤波算法能够较好的提高对机动目标的跟踪精度。
Target tracking whether in military or civilian fields has been used widely. For instance in air defense and in air traffic control.With the rapid development of aerospace technology,all flight vehicles’speed and maneuverability are higher and higher. Under this background, how to enhance the performance of tracking of high speed and high maneuvering target has become a more important issue. The maneuvering target can worse the performance of the tracking systems ,because of the complexity ,the randomness and the multiplicity of maneuvering,it has a higher technical difficulty whether in the theory and practice for maneuvering targets tracking. Therefore, this paper based on the achievements summed up by our predecessors, focused on the study of high maneuvering target tracking problem.
The development procedure of maneuvering target is first reviewed in paper, the theoretical knowledge of the target tracking is introduced in detail: parameter estimation and state estimation, Kalman filter, and discussed the problems in Kalman filtering. On this basis, the related issues are researched. Firstly, the Kalman filter needs to know the model of the state noise and observation noise, however, the aim of the prior statistical information which is rare for describing accurately. At this point, the prior statistic characteristics of personal supposed moving object is easy to lead to personal errors for object tracking. Second, in moving target, object model of the maneuver frequency values are generally based on experience, it also will lead to personal error.
Taking these two issues, this paper presents the model based on Jerk Maneuvering Target Tracking Algorithm:
1.The first to use System Identification Parameter Identification of relevant knowledge, combined with Jerk Model to mention apart from motor frequency identification method, and because the presence of observation noise, making identification of the non-bias can not meet, so the proposed identification algorithm is a reasonable compensation. Experimental results show that the method can target motion model in the mobile frequency sound recognition.
2.Knowledge according to the system identification, parameters, the system can also be viewed as a state, so the motor frequency as the system state equation of a state model was proposed at the same time due to the presence of non-linear, combined with non-filtered Minkaerman processing, through a large number of experiments show that the model and the corresponding filtering algorithm can improve the tracking accuracy of maneuvering targets
本文首先回顾了机动目标跟踪问题的发展历程和研究现状,详细介绍了目标跟踪的理论知识:状态估计、参数估计与状态估计、卡尔曼滤波等滤波方法。在此基础上,对相关的问题进行了研究,由于运动目标的高速高机动性,迫切要求我们能够更加准切的描述目标的模型,机动频率是描述运动目标机动性的参数,该参数一般是根据人们的经验认为设定的,因此,在对运动目标的跟踪过程中不可避免的引入人为误差,降低了跟踪精度。
本文主要研究的高机动高速运动目标的跟踪问题,开展了一下研究工作:
1、首先利用系统辨识中参数辨识的有关知识,结合Jerk模型提出了机动频率的辨识方法,同时由于观测噪声的存在,使得辨识的无偏性不能满足,因此对辨识算法提出了合理的补偿方法。实验结果表明,该方法能对目标运动模型中的机动频率进行良好的辨识。
2、根据系统辨识相关知识,参数也可以看作系统的一个状态,因此将机动频率作为系统状态方程中的一个状态进行建模,提出了α? CS ? Jerk模型,同时由于非线性的存在,结合不敏卡尔曼进行滤波处理,通过仿真表明,该模型和相应的滤波算法能够较好的提高对机动目标的跟踪精度。
Target tracking whether in military or civilian fields has been used widely. For instance in air defense and in air traffic control.With the rapid development of aerospace technology,all flight vehicles’speed and maneuverability are higher and higher. Under this background, how to enhance the performance of tracking of high speed and high maneuvering target has become a more important issue. The maneuvering target can worse the performance of the tracking systems ,because of the complexity ,the randomness and the multiplicity of maneuvering,it has a higher technical difficulty whether in the theory and practice for maneuvering targets tracking. Therefore, this paper based on the achievements summed up by our predecessors, focused on the study of high maneuvering target tracking problem.
The development procedure of maneuvering target is first reviewed in paper, the theoretical knowledge of the target tracking is introduced in detail: parameter estimation and state estimation, Kalman filter, and discussed the problems in Kalman filtering. On this basis, the related issues are researched. Firstly, the Kalman filter needs to know the model of the state noise and observation noise, however, the aim of the prior statistical information which is rare for describing accurately. At this point, the prior statistic characteristics of personal supposed moving object is easy to lead to personal errors for object tracking. Second, in moving target, object model of the maneuver frequency values are generally based on experience, it also will lead to personal error.
Taking these two issues, this paper presents the model based on Jerk Maneuvering Target Tracking Algorithm:
1.The first to use System Identification Parameter Identification of relevant knowledge, combined with Jerk Model to mention apart from motor frequency identification method, and because the presence of observation noise, making identification of the non-bias can not meet, so the proposed identification algorithm is a reasonable compensation. Experimental results show that the method can target motion model in the mobile frequency sound recognition.
2.Knowledge according to the system identification, parameters, the system can also be viewed as a state, so the motor frequency as the system state equation of a state model was proposed at the same time due to the presence of non-linear, combined with non-filtered Minkaerman processing, through a large number of experiments show that the model and the corresponding filtering algorithm can improve the tracking accuracy of maneuvering targets
引文
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[8] E.Mazor,J.Dayan,Y.Bar-Shalom.Interacting Multiple Model in Target Tracking:a Survey[J].IEEE Transactions on Aerospace and Electronic Systems,1998,10(3):103-124
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[11] Mahapatra P.R.,Mehrotra K.Mixed Coordinate Tracking of Generalized Maneuvering Targets Using Acceleration and Jerk Models[J].IEEE Transactions on Aerospace and Electronic Systems,2000,36(3):992-1000
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[22]梁彦,潘泉,张洪才,戴贯中,常增益交互式多模型算法,控制理论及应用,1999,16(5):659-663
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[32] Simon Julier,Jeffrey Uhlmann.A New Extension of the Kalman Filter to Nonlinear System.SPIE,vol.3068,1997:182-193
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[34] Merwe R.,E.A.Wan.Efficient Derivative-Free Kalman Filters for Online Learning, In European Symposium on Artificial Neural Networks,2001:205-210
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[36] J.Levine,R.Marino,Constant-Speed Target Tracking Via Bearings-Only Measurements.IEEE Trans.On AES,1999,28(1):174-181
[37]王淑一,程杨,杨涤,崔祻涛.UKF方法及其在方位跟踪问题中的应用.飞行力学,2003,2(2):59-62
[38] Qiu B S,Liu H Y,Cai Y Q,et al.An adaptive parameter model for maneuvering target tracking.In:Proceedings of the 4th International Conference on Signal Processing,ICSP'98.Beijing:IEEE,1998:465-469
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[2]何友,修建娟,张晶炜等.雷达数据处理[M].北京:电子工业出版社, 2006.12-31,139-154
[3]杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社, 2004.52-59
[4] Singer R A.Estimating optimal tracking filter performance for manned maneuvering targets.IEEE Transactions on Aerospace and Electronic Systems,1970,Vol.AES-6, No.4:473-483
[5] Bar-Shalom Y.,Birmiwal K.Variable dimension filter for maneuvering target tracking[J].IEEE Transactions on Aerospace and Electronic System, 1982, 18(5):611-619
[6] Blom H.A.P.,Bar-Shalom Y.The interacting multiple model algorithm for systems with markovian switching coefficients[J].IEEE Transactions on Automatic Control,1988,33(8):780-783
[7] Blom H.A.P.,Bar-Shalom Y.The interacting multiple model algorithm for systems with markovian switching coefficients[J].IEEE Transactions on Automatic Control,1988,33(8):780-783
[8] E.Mazor,J.Dayan,Y.Bar-Shalom.Interacting Multiple Model in Target Tracking:a Survey[J].IEEE Transactions on Aerospace and Electronic Systems,1998,10(3):103-124
[9] Feng X.X.,Liu Z.L.Maneuvering target tracking using real-time input estimation[J].In Proceedings of the 1996 CIE international Conference of Rader.Beijing:IEEE,1996:731-734
[10] Kishore Mehrotra,Pravas R.Mahapatra.A Jerk Model for Tracking Highly Maneuvering Targets[J].IEEE Transactions on Aerospace and Electronics Syste 997,33(4):1094-11,19
[11] Mahapatra P.R.,Mehrotra K.Mixed Coordinate Tracking of Generalized Maneuvering Targets Using Acceleration and Jerk Models[J].IEEE Transactions on Aerospace and Electronic Systems,2000,36(3):992-1000
[12]王亚利,林家骏.基于粒子滤波的机动目标跟踪[J].计算机工程与应用, 2007,43(34):61-64
[13]徐金豹.基于新的粒子滤波算法的机动目标跟踪研究[D].硕士论文,南京:南京航空航天大学,2008
[14]许录平.跟踪机动目标的自适应α?β滤波算法[J].西安电子科技大学学报,1998,25(3):314-317
[15]金宏斌,徐毓,钱琼芬.机动目标跟踪的改进α?β算法[J].空军雷达学院学报,2003,17(2):27-29
[16]王红亮,李枫,赵亦工.一种机动目标跟踪的自适应α?β滤波算法[J].雷达科学与技术,2007,5(4):278-282
[17]唐劲松,何友,王国宏.一种自适应α?β?γ滤波器[J].火力与指挥控制,1995,20(2):32-36
[18]郭军海,高耀文,沙定果.多级组合α?β?γ跟踪滤波器[J].弹道学报,2003,15(2):83-88
[19]乔向东,王宝树,李涛等.一种高度机动目标的“当前”统计Jerk模型[J].西安电子科技大学学报(自然科学版),2002,29(4):534-539
[20]雷明,韩崇昭.多级修正的高机动Jerk模型研究[J].西安交通大学学报, 2006,40(2):138-141
[21]李大治,李刚,辛勤等.一种基于Jerk模型的机动目标单站无源定位算法[J].国防科技大学学报,2007,29(4):82-86
[22]梁彦,潘泉,张洪才,戴贯中,常增益交互式多模型算法,控制理论及应用,1999,16(5):659-663
[23]陈亮.机动目标跟踪算法研究. [D].硕士论文,无锡:江南大学,2008
[24]王秀峰,卢桂章编著,北京:电子工业出版社,2004.33-34
[25]张晶炜.多传感器多目标跟踪算法性能分析、比较及研究[D].硕士论文,烟台:海军航空工程学院,2004
[26] Benedict T.G Bordener Synthesis of an Optimal Set of Radar Track-While -Scan Smoothing Equations.IRE Trans.on Automatical Control, AC-97, July, 1962,27-32
[27]沈莹.机动目标跟踪算法与应用研究.[D].硕士论文,西北工业大学,2007
[28] Kalata P.R.The Tracking Index:A Generalized Parameter forα-βandα-β-γTarget Trackers[J].IEEE Transactions on Aerospace and Electronic Systems, 1984, 20(2): 174-182
[29]刘明.TWS系统中相关方法的研究[D].硕士论文,西北电讯工程学院,1985
[30]程永梅,潘泉,张宏才等.基于扩展卡尔曼滤波的多站被动式融合跟踪.系统仿真学报,2003,15(4):548-550
[31]崔少辉,戴明桢.利用推广卡尔曼滤波实现纯方位目标跟踪.南京航空学院学报,June 1989,21
[32] Simon Julier,Jeffrey Uhlmann.A New Extension of the Kalman Filter to Nonlinear System.SPIE,vol.3068,1997:182-193
[33] Simon Julier,Jeffrey Uhlmann.A New Method for the Nonlinear Transfor- mation of Means and Covariances in Filters and Estimations.IEEE Trans.on AC,45(3),No.3 2000:477-482
[34] Merwe R.,E.A.Wan.Efficient Derivative-Free Kalman Filters for Online Learning, In European Symposium on Artificial Neural Networks,2001:205-210
[35] M,c. VanDyke, J.L. Schwartz,C.D.Hall.Unscented Kalman Filtering for Spacecraft Attitude State and Parameter Estimation.AAS/AIAA Space Flight Mechanics Conference,2004
[36] J.Levine,R.Marino,Constant-Speed Target Tracking Via Bearings-Only Measurements.IEEE Trans.On AES,1999,28(1):174-181
[37]王淑一,程杨,杨涤,崔祻涛.UKF方法及其在方位跟踪问题中的应用.飞行力学,2003,2(2):59-62
[38] Qiu B S,Liu H Y,Cai Y Q,et al.An adaptive parameter model for maneuvering target tracking.In:Proceedings of the 4th International Conference on Signal Processing,ICSP'98.Beijing:IEEE,1998:465-469
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