基于自适应滤波的机动目标跟踪算法研究
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摘要
目标跟踪无论在空中侦察与预警、弹道导弹防御、战场监视等军事领域,还是空中交通管制、交通导航等民用领域均有广泛的应用,扮演着重要的角色。然而随着现今各类飞行器机动性能的提高,传统的目标跟踪算法已逐渐不能满足精确跟踪的要求,应用自适应滤波提高机动目标跟踪的性能,是一项颇具现实意义的研究课题。
机动目标跟踪自适应滤波算法主要分三种类型,检测自适应滤波、实时辨识自适应滤波与全面自适应滤波,本文首先对三种算法中各自典型算法:变维算法(VDF)、当前统计模型均值自适应算法、交互式多模型算法(IMM)算法进行研究,通过仿真分析出综合性能上IMM算法有着明显的优势,而当前统计模型均值算法在高机动的情况下跟踪性能良好。
当前统计模型算法存在着两个缺陷:弱机动目标跟踪能力差;目标实现跟踪需要对目标机动加速度进行合理的先验假设。本文深入研究当前统计自适应算法原理,应用模糊机理对当前统计自适应算法中先验假设进行适当修正,仿真表明其在一定程度上弱化了当前统计模型的缺陷。
针对IMM算法中模型转移概率矩阵为固定值的情况,应用滤波过程中得到的模型概率,实时对模型状态转移矩阵进行修正,并对基于常速(CV)、常加速(CA)、机动转弯模型(CT)的改进IMM算法与传统IMM算法进行了对比仿真,验证了改进算法具备较高的精度,机动调节时间也相应减少。最后考虑到当前统计模型对高机动目标的良好跟踪能力,将改进当前统计模型融入修正转移概率的IMM算法中,结合用以描述非机动、弱机动的CV、CA模型作为IMM算法子模型集,增强了IMM算法的自适应性。
Maneuvering Target Tracking is particularly important in the field of national defense research. In the air reconnaissance and warning, missile defense, battlefield surveillance and other military fields, as well as air traffic control, navigation and other civilian traffic areas, Maneuvering Target Track is a key technology. Now as the mobility of modern aircrafts increasing, the traditional methods of target tracking could not satisfy the request of tracking accurate at all. So using the adaptive filtering algorithms to improve the performance of Maneuvering Target Tracking is a meaningful research work.
The basic adaptive filtering algorithms of Maneuvering Target Tracking can divide into three parts: Examines adaptive filtering, Real-time identification adaptive filtering and Comprehensive adaptive filtering. In this paper we research on Variable Dimension Filtering algorithm, Current Statistical Model Adaptive algorithm and Interactive Multiple Model (IMM) which belongs to the basic algorithms respectively. Through the simulation analysis, IMM algorithm has the obvious superiority in the comprehensive performance, and Current Statistical Model algorithm in the case of large maneuvering can get good tracking performance.
Current Statistical Model algorithm has two flaws. One is the algorithm isn’t suitable for slight maneuvering, and the other is algorithm needs reasonable transcendental hypothesis for the acceleration of targets. Based on the study of Current Statistical Model algorithm, an improved Current Statistical Model algorithm using fuzzy theory is proposed to revise the two flaws reasonably. And the algorithm is proved by computer simulation.
In the traditional IMM algorithm the model probability transfer matrix is a fixture. Here we proposed a new algorithm. This algorithm using the model probability, which obtains from the process of IMM algorithm, revises the model probability transfer matrix on time. For comparing with the traditional algorithm we set an IMM algorithm with CV, CA, CT three types of model which is used to simulate. From the results of simulation, we can obtain the new method can get high accurate and less adjust time. At last considering the good tracking ability of the improved Current Statistical Model algorithm, we used the improved Current Statistical Model as a sub-model in new IMM algorithm to describe the situation of target maneuvering so that to expand the adaptive performance of IMM algorithm.
机动目标跟踪自适应滤波算法主要分三种类型,检测自适应滤波、实时辨识自适应滤波与全面自适应滤波,本文首先对三种算法中各自典型算法:变维算法(VDF)、当前统计模型均值自适应算法、交互式多模型算法(IMM)算法进行研究,通过仿真分析出综合性能上IMM算法有着明显的优势,而当前统计模型均值算法在高机动的情况下跟踪性能良好。
当前统计模型算法存在着两个缺陷:弱机动目标跟踪能力差;目标实现跟踪需要对目标机动加速度进行合理的先验假设。本文深入研究当前统计自适应算法原理,应用模糊机理对当前统计自适应算法中先验假设进行适当修正,仿真表明其在一定程度上弱化了当前统计模型的缺陷。
针对IMM算法中模型转移概率矩阵为固定值的情况,应用滤波过程中得到的模型概率,实时对模型状态转移矩阵进行修正,并对基于常速(CV)、常加速(CA)、机动转弯模型(CT)的改进IMM算法与传统IMM算法进行了对比仿真,验证了改进算法具备较高的精度,机动调节时间也相应减少。最后考虑到当前统计模型对高机动目标的良好跟踪能力,将改进当前统计模型融入修正转移概率的IMM算法中,结合用以描述非机动、弱机动的CV、CA模型作为IMM算法子模型集,增强了IMM算法的自适应性。
Maneuvering Target Tracking is particularly important in the field of national defense research. In the air reconnaissance and warning, missile defense, battlefield surveillance and other military fields, as well as air traffic control, navigation and other civilian traffic areas, Maneuvering Target Track is a key technology. Now as the mobility of modern aircrafts increasing, the traditional methods of target tracking could not satisfy the request of tracking accurate at all. So using the adaptive filtering algorithms to improve the performance of Maneuvering Target Tracking is a meaningful research work.
The basic adaptive filtering algorithms of Maneuvering Target Tracking can divide into three parts: Examines adaptive filtering, Real-time identification adaptive filtering and Comprehensive adaptive filtering. In this paper we research on Variable Dimension Filtering algorithm, Current Statistical Model Adaptive algorithm and Interactive Multiple Model (IMM) which belongs to the basic algorithms respectively. Through the simulation analysis, IMM algorithm has the obvious superiority in the comprehensive performance, and Current Statistical Model algorithm in the case of large maneuvering can get good tracking performance.
Current Statistical Model algorithm has two flaws. One is the algorithm isn’t suitable for slight maneuvering, and the other is algorithm needs reasonable transcendental hypothesis for the acceleration of targets. Based on the study of Current Statistical Model algorithm, an improved Current Statistical Model algorithm using fuzzy theory is proposed to revise the two flaws reasonably. And the algorithm is proved by computer simulation.
In the traditional IMM algorithm the model probability transfer matrix is a fixture. Here we proposed a new algorithm. This algorithm using the model probability, which obtains from the process of IMM algorithm, revises the model probability transfer matrix on time. For comparing with the traditional algorithm we set an IMM algorithm with CV, CA, CT three types of model which is used to simulate. From the results of simulation, we can obtain the new method can get high accurate and less adjust time. At last considering the good tracking ability of the improved Current Statistical Model algorithm, we used the improved Current Statistical Model as a sub-model in new IMM algorithm to describe the situation of target maneuvering so that to expand the adaptive performance of IMM algorithm.
引文
1周宏仁,敬忠良.机动目标跟踪.国防工业出版社, 1991:2~8 56~74 134~176
2何友,修建娟.雷达数据处理及应用.电子工业出版社, 2006:1~7 17~30 134~154
3 Singer R. A, Estimating optimal tracking filter performance for Manned Maneuvering Targets, IEEE Transactions on Aerospace and Electronic Systems, 1970, 6: 473~483
4 Moose R. L, Vanlandingham H. F, McCabe D.H, Modeling and estimation for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1979, Vol.AES~15, No.3: 448~456
5 Kendrick J. D. Estimation of aircraft tracking motion using orientation measurements. IEEE Trans. Aerospace and Electronic System, 1981.AES-17: 254~259
6 Mahapatra P. R, Mehrotra K. Mixed coordinate tracking of generalized maneuvering targets using acceleration and jerk models. IEEE Transactions on Aerospace and Electronic Systems, 2000, Vol. 36, No.3: 992~1000
7周宏仁.机动目标当前统计模型与自适应跟踪算法.航空学报. 1983, 4:
73~86
8李涛,王宝树.基于截断正态概率模型的改进目标跟踪算法.系统工程与电子技术, 2003, 25:1289~1291
9 G. A. Ackerso, K. S. Fu. On State Estimation in Switching Environments J. IEEE Trans. on Automatic Control, 1970, AC-15:10~17
10 Magill D. T. Optimal adaptive estimation of sampled stochastic processes. IEEE Transactions on Automatic Control, 1965, Vol. AC~10, No4:434~439
11 Blom H. A. P. An efficient filter for abruptly changing systems, Proceedings of 23rd Conference on Decision and Control. Las Vegas: IEEE, 1984:656~658
12 X. R. Li, Y Bar-Shalom. Multiple-Model Estimation with Variable Structure. IEEE Tram on Automatic Control, 1996, 41:478~493
13 X. R. Li. Engineer’s Guide to Variable-Structure Multiple-Model Estimationfor Tracking. Multitarget-Multisensor Tracking: Applications and Advances, Boston, MA: Arteeh House, 2000:499~567
14 Watson G. A, Blair W. D. Interacting acceleration compensation algorithm for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31:1152~1159
15 Johoston L. A, Krishnamurthy V. An improvement to the interacting multiple model algorithm. IEEE Transactions on Signal Processing, 2001, Vol. 49, No.12:2909~2923
16张跃雄,李敏勇.自适应交互式多模型算法在机动目标跟踪中的应用.情报指挥控制系统与仿真. 2005, 27:26~42
17陈康,嵇成新.雷达跟踪机动目标的多模型算法研究.舰船科学技术. 2004, 2:54~57
18陈利斌.机动目标跟踪理论和算法的研究.西北工业大学博士论文. 2007: 1~2
19康凤举.现代仿真技术与应用.国防工业出版社,2001
20 Kalman R. E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering Tran. 1960, 82(1):35~45
21 Maybeck R. J, Denlinger E. A decision-directed adaptive tracker. IEEE Transactions on Aerospace and Electronic Systems, 1973, 9:229~236
22 Thorp J. S, Optimal tracking of maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1973, 9:512~519
23 Chan Y T, Hu A.G. A Kalman filter based tracking scheme with input estimation. IEEE Transactions on Aerospace and Electronic Systems, 1979, 15: 237~244
24 Bar-Shalom Y, Birmiwal K. Variable dimension filter for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic System, 1982, 18: 611~619
25陈晓荣,陈淑芬.变维卡尔曼滤波实现运动目标的跟踪.仪器仪表学报,2006, 27:1163~1166
26 Mehra K. K. Approach to adaptive filtering. IEEE Transactions on Automatic Control, 1972, 17:693~698
27 Moose R. L. An adaptive state estimation solution to the maneuvering target problem. IEEE Transactions on Automatic Control, 1975, 70:359~362
28潘平俊,冯新喜.机动目标跟踪模型研究与发展综述.指挥控制与仿真. 2006, 3:13~15
29王芳,冯新喜.一种新的自适应滤波算法.现代雷达. 2003, 7:33~35
30李辉,沈莹.机动目标跟踪中一种新的自适应滤波算法.西北工业大学学报. 2006, 24:354~357
31胡振涛,刘先省.基于当前统计模型的一种改进机动目标跟踪算法.山东大学学报. 2005, 35:112~124
32刁联旺,杨靖宇.一种改进当前统计模型描述.兵工学报. 2005, 26:826~828
33段战胜,韩崇昭.最大加速度未知的当前统计模型机动目标跟踪.计算机工程与应用. 2003, 29:20~22
34罗笑冰,王宏强.非线性当前统计模型及自适应滤波算法.系统工程与电子技术. 2008, 30:398~403
35 Houles, Y. Bar-Shalom. Multisensor tracking of a maneuvering target in clutter. IEEE Trans. on Aerospace and Electronic Systems, 1989, 25(2):176~188
36李辉,赵敏.交互式多模型算法在机动目标跟踪中的应用.弹箭与制导学报. 2005, 3:234~236
37 Rabiner L. R, A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, Vol. 77, No.2: 257~286
38 P. Mookerjee, L. Campo, Estimation in systems with a semi-Markov switching model. In Proceeding of the 26th Conference on Decision and Control, Los Angeles, 1987, 10:332~334
39罗笑冰,王宏强.模型转移概率自适应的交互式多模型跟踪算法.电子与信息学报. 2008, 20:1540~1544
40臧荣春,崔平远.马尔可夫自适应IFMM算法研究.电子学报. 2006, 3: 522~524
41陈利斌,佟明安.机动目标跟踪的交互式多模型自适应滤波算法.火力与指挥控制. 2000, 12:37~42
2何友,修建娟.雷达数据处理及应用.电子工业出版社, 2006:1~7 17~30 134~154
3 Singer R. A, Estimating optimal tracking filter performance for Manned Maneuvering Targets, IEEE Transactions on Aerospace and Electronic Systems, 1970, 6: 473~483
4 Moose R. L, Vanlandingham H. F, McCabe D.H, Modeling and estimation for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1979, Vol.AES~15, No.3: 448~456
5 Kendrick J. D. Estimation of aircraft tracking motion using orientation measurements. IEEE Trans. Aerospace and Electronic System, 1981.AES-17: 254~259
6 Mahapatra P. R, Mehrotra K. Mixed coordinate tracking of generalized maneuvering targets using acceleration and jerk models. IEEE Transactions on Aerospace and Electronic Systems, 2000, Vol. 36, No.3: 992~1000
7周宏仁.机动目标当前统计模型与自适应跟踪算法.航空学报. 1983, 4:
73~86
8李涛,王宝树.基于截断正态概率模型的改进目标跟踪算法.系统工程与电子技术, 2003, 25:1289~1291
9 G. A. Ackerso, K. S. Fu. On State Estimation in Switching Environments J. IEEE Trans. on Automatic Control, 1970, AC-15:10~17
10 Magill D. T. Optimal adaptive estimation of sampled stochastic processes. IEEE Transactions on Automatic Control, 1965, Vol. AC~10, No4:434~439
11 Blom H. A. P. An efficient filter for abruptly changing systems, Proceedings of 23rd Conference on Decision and Control. Las Vegas: IEEE, 1984:656~658
12 X. R. Li, Y Bar-Shalom. Multiple-Model Estimation with Variable Structure. IEEE Tram on Automatic Control, 1996, 41:478~493
13 X. R. Li. Engineer’s Guide to Variable-Structure Multiple-Model Estimationfor Tracking. Multitarget-Multisensor Tracking: Applications and Advances, Boston, MA: Arteeh House, 2000:499~567
14 Watson G. A, Blair W. D. Interacting acceleration compensation algorithm for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31:1152~1159
15 Johoston L. A, Krishnamurthy V. An improvement to the interacting multiple model algorithm. IEEE Transactions on Signal Processing, 2001, Vol. 49, No.12:2909~2923
16张跃雄,李敏勇.自适应交互式多模型算法在机动目标跟踪中的应用.情报指挥控制系统与仿真. 2005, 27:26~42
17陈康,嵇成新.雷达跟踪机动目标的多模型算法研究.舰船科学技术. 2004, 2:54~57
18陈利斌.机动目标跟踪理论和算法的研究.西北工业大学博士论文. 2007: 1~2
19康凤举.现代仿真技术与应用.国防工业出版社,2001
20 Kalman R. E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering Tran. 1960, 82(1):35~45
21 Maybeck R. J, Denlinger E. A decision-directed adaptive tracker. IEEE Transactions on Aerospace and Electronic Systems, 1973, 9:229~236
22 Thorp J. S, Optimal tracking of maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1973, 9:512~519
23 Chan Y T, Hu A.G. A Kalman filter based tracking scheme with input estimation. IEEE Transactions on Aerospace and Electronic Systems, 1979, 15: 237~244
24 Bar-Shalom Y, Birmiwal K. Variable dimension filter for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic System, 1982, 18: 611~619
25陈晓荣,陈淑芬.变维卡尔曼滤波实现运动目标的跟踪.仪器仪表学报,2006, 27:1163~1166
26 Mehra K. K. Approach to adaptive filtering. IEEE Transactions on Automatic Control, 1972, 17:693~698
27 Moose R. L. An adaptive state estimation solution to the maneuvering target problem. IEEE Transactions on Automatic Control, 1975, 70:359~362
28潘平俊,冯新喜.机动目标跟踪模型研究与发展综述.指挥控制与仿真. 2006, 3:13~15
29王芳,冯新喜.一种新的自适应滤波算法.现代雷达. 2003, 7:33~35
30李辉,沈莹.机动目标跟踪中一种新的自适应滤波算法.西北工业大学学报. 2006, 24:354~357
31胡振涛,刘先省.基于当前统计模型的一种改进机动目标跟踪算法.山东大学学报. 2005, 35:112~124
32刁联旺,杨靖宇.一种改进当前统计模型描述.兵工学报. 2005, 26:826~828
33段战胜,韩崇昭.最大加速度未知的当前统计模型机动目标跟踪.计算机工程与应用. 2003, 29:20~22
34罗笑冰,王宏强.非线性当前统计模型及自适应滤波算法.系统工程与电子技术. 2008, 30:398~403
35 Houles, Y. Bar-Shalom. Multisensor tracking of a maneuvering target in clutter. IEEE Trans. on Aerospace and Electronic Systems, 1989, 25(2):176~188
36李辉,赵敏.交互式多模型算法在机动目标跟踪中的应用.弹箭与制导学报. 2005, 3:234~236
37 Rabiner L. R, A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, Vol. 77, No.2: 257~286
38 P. Mookerjee, L. Campo, Estimation in systems with a semi-Markov switching model. In Proceeding of the 26th Conference on Decision and Control, Los Angeles, 1987, 10:332~334
39罗笑冰,王宏强.模型转移概率自适应的交互式多模型跟踪算法.电子与信息学报. 2008, 20:1540~1544
40臧荣春,崔平远.马尔可夫自适应IFMM算法研究.电子学报. 2006, 3: 522~524
41陈利斌,佟明安.机动目标跟踪的交互式多模型自适应滤波算法.火力与指挥控制. 2000, 12:37~42