基于新的粒子滤波算法的机动目标跟踪研究
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摘要
机动目标跟踪在国防科研、雷达信号处理及其他相关领域中是一个非常重要的研究课题,最近几十年来,国内外众多专家学者对之进行了深入的研究,并取得了丰硕的成果,其中部分研究成果已经成功应用于空中侦察与预警、弹道导弹防御、战场监视等军事领域,以及空中交通管制、交通导航、机器人视觉系统等民用领域。本论文主要对机动目标跟踪中的非线性滤波算法进行了系统深入的研究。
首先系统地概述了机动目标跟踪的基本原理,讨论了几种常用的目标机动模型、基本滤波预测方法和数据关联算法。分析比较了多种非线性滤波算法和新的粒子滤波算法,实验结果表明代价参考粒子滤波算法有较好的滤波性能。
其次为解决非线性非高斯条件下的机动目标跟踪问题,论文将代价参考粒子滤波算法与当前统计模型的优点相结合,提出了基于代价参考粒子滤波的“当前”统计模型自适应跟踪算法,仿真结果表明该算法用于解决机动目标跟踪问题是有效可行的;研究了基于代价参考粒子滤波的多模型跟踪算法,对该算法重采样过程随机性太大的缺陷做了改进,并通过仿真实验证明了改进算法的有效性和可行性;分析比较了多模型跟踪算法中模型数目选取对跟踪性能的影响,并对基于代价参考粒子滤波的两种跟踪算法的跟踪性能做了对比分析。
最后针对多目标跟踪中的数据关联问题,阐述了几种典型的多目标数据关联算法,研究了另一种新的粒子滤波算法,即Rao-Blackwellised粒子滤波算法,然后将该算法应用于单机动目标的跟踪问题和多机动目标的数据关联问题,仿真结果表明,该算法有较高的跟踪精度和较好的实时性。论文还提出今后机动目标跟踪的发展方向。
The maneuvering target tracking is an extremely important research topic in national defense, the processing of radar signals and other related areas. In recent years, target tracking technology has been widely studied, and has made plentiful and substantial achievements. Some research results have been widely applied to military fields, such as air reconnaissance and early warning, ballistic missile defense, battlefield surveillance, etc. Some research results have been applied to civil fields, such as air traffic control, traffic navigation and robot vision system, etc. This paper researches nonlinear filtering algorithms of maneuvering target tracking more systemic.
Firstly, basic principle of maneuvering target tracking is summarized and some familiar target maneuvering models, basic filtering and data association algorithms are also discussed. Following the discussion, this paper analyzes and compares several nonlinear filtering algorithms, the simulation results demonstrate the Cost Reference particle filter has an excellence filtering performances.
Secondly, in order to solve nonlinear and non-Gaussian maneuvering target tracking problems, this paper integrates the advantages of the Cost Reference particle filter with the Current Statistical Model, and proposes a new current statistical model adaptive tracking algorithm, and the simulation results demonstrate its availability for maneuvering target tracking. This paper also discusses the multiple models adaptive tracking algorithm based on Cost Reference particle filter, and analyzes the blemish that the over randomicity of resample algorithm, as a result this paper puts forward a improved algorithm and the simulation validates the availability and applicability of the improved algorithm. The effects on algorithm performances by the amount of selected models in the multiple models tracking algorithm are investigated, and the tracking performances of the two tracking algorithms based on Cost Reference particle filter are compared through separate simulation in this paper.
Finally, aiming at the data association problems of multiple targets, this paper expatiates some representative algorithms of multiple targets data association. Another new Rao-Blackwellised Particle Filter algorithm is discussed in details, and then is applied to solve the single maneuvering target tracking problem and the data association problem of multiple targets. The simulation results demonstrate the algorithm has highly real-time performance and the tracking accuracy rate. This paper also presents the future development of maneuvering target tracking.
首先系统地概述了机动目标跟踪的基本原理,讨论了几种常用的目标机动模型、基本滤波预测方法和数据关联算法。分析比较了多种非线性滤波算法和新的粒子滤波算法,实验结果表明代价参考粒子滤波算法有较好的滤波性能。
其次为解决非线性非高斯条件下的机动目标跟踪问题,论文将代价参考粒子滤波算法与当前统计模型的优点相结合,提出了基于代价参考粒子滤波的“当前”统计模型自适应跟踪算法,仿真结果表明该算法用于解决机动目标跟踪问题是有效可行的;研究了基于代价参考粒子滤波的多模型跟踪算法,对该算法重采样过程随机性太大的缺陷做了改进,并通过仿真实验证明了改进算法的有效性和可行性;分析比较了多模型跟踪算法中模型数目选取对跟踪性能的影响,并对基于代价参考粒子滤波的两种跟踪算法的跟踪性能做了对比分析。
最后针对多目标跟踪中的数据关联问题,阐述了几种典型的多目标数据关联算法,研究了另一种新的粒子滤波算法,即Rao-Blackwellised粒子滤波算法,然后将该算法应用于单机动目标的跟踪问题和多机动目标的数据关联问题,仿真结果表明,该算法有较高的跟踪精度和较好的实时性。论文还提出今后机动目标跟踪的发展方向。
The maneuvering target tracking is an extremely important research topic in national defense, the processing of radar signals and other related areas. In recent years, target tracking technology has been widely studied, and has made plentiful and substantial achievements. Some research results have been widely applied to military fields, such as air reconnaissance and early warning, ballistic missile defense, battlefield surveillance, etc. Some research results have been applied to civil fields, such as air traffic control, traffic navigation and robot vision system, etc. This paper researches nonlinear filtering algorithms of maneuvering target tracking more systemic.
Firstly, basic principle of maneuvering target tracking is summarized and some familiar target maneuvering models, basic filtering and data association algorithms are also discussed. Following the discussion, this paper analyzes and compares several nonlinear filtering algorithms, the simulation results demonstrate the Cost Reference particle filter has an excellence filtering performances.
Secondly, in order to solve nonlinear and non-Gaussian maneuvering target tracking problems, this paper integrates the advantages of the Cost Reference particle filter with the Current Statistical Model, and proposes a new current statistical model adaptive tracking algorithm, and the simulation results demonstrate its availability for maneuvering target tracking. This paper also discusses the multiple models adaptive tracking algorithm based on Cost Reference particle filter, and analyzes the blemish that the over randomicity of resample algorithm, as a result this paper puts forward a improved algorithm and the simulation validates the availability and applicability of the improved algorithm. The effects on algorithm performances by the amount of selected models in the multiple models tracking algorithm are investigated, and the tracking performances of the two tracking algorithms based on Cost Reference particle filter are compared through separate simulation in this paper.
Finally, aiming at the data association problems of multiple targets, this paper expatiates some representative algorithms of multiple targets data association. Another new Rao-Blackwellised Particle Filter algorithm is discussed in details, and then is applied to solve the single maneuvering target tracking problem and the data association problem of multiple targets. The simulation results demonstrate the algorithm has highly real-time performance and the tracking accuracy rate. This paper also presents the future development of maneuvering target tracking.
引文
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[2]何友,修建娟,张晶炜等,雷达数据处理及应用,北京,电子工业出版社, 2006:12-155
[3]杨万海,多传感器数据融合及其应用,西安,西安电子科技大学出版社, 2004:33-167
[4]何友,陆大,彭应宁,等.多传感器信息融合及应用,北京.电子工业出版社, 2000:29-172
[5]朱洪艳.机动目标跟踪理论及其应用研究, [博士学位论文].西安.西安交通大学, 2003:2-68
[6]刘刚,多目标跟踪算法及实现研究, [博士学位论文],西安,西北工业大学, 2003:3-55
[7] Wax.N, Singal-to-noise Improvement and the Statistics of Tracking Populations, Journal of Applied Physics, 1955, 26(5): 586-595
[8] R.N.Sittler, An Optimal Data Association Problem in Surveillance Theory, IEEE Transactions on Military Electronics, 1964, 8(2): 125-139
[9] Y.Bar-Shalom, Tracking Methods in A Multi-target Environment, IEEE Trans.on AC, 1978, 23(4): 618-628
[10] R.A.Singer, Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets, IEEE Transactions on Aerospace and Electronic Systems (AES), 1970, 6(4): 473-484
[11] B Friedland,Optimum steady-state position and velocity estimation using noisy sampled position data,IEEE Transactions on Aerospace and Electronic Systems(AES),1973,9(6):906-1101
[12] R L T Hampton, J R Cooke,Unsupervised tracking of maneuvering vehicle,IEEE Transactions on Aerospace and Electronic Systems(AES), 1973, 9(2):197-207
[13] Kalman R E,A new approach to linear filtering and prediction problem Trans . ASME. J. Basic Engineering, 1960, 82: 34-45
[14] H.Chen, T.Kirubarajan, Y.Bar-Shalom. Performance Limits of Track-to-Track Fusion Versus centralized Estimation. Theory and Application, IEEE Trans, On Aerospace and Electronic Systems, 2003, 39(2): 386-400
[15]张永胜,嵇成新,一种基于当前统计模型的模糊交互式多模型算法.火力与指挥控制, 2003, 28(1): 51-55
[16] Spingarn K,Weidemann H J,Linear regression filtering and prediction for tracking maneuvering aircraft targets, IEEE Transactions on Aerospace and Electronic Systems, 1972,8(6):800~810
[17] Singer R A, Behnke K W, Real-time tracking filter evaluation and selection for tactical applications, IEEE Transactions on Aerospace and Electronic Systems, 1971, 7(1):100-110
[18] Singer RA, Froast PA, On the relative performance of the Kalman and Winner filters IEEE Transactions on Automatic Control 1959, 14(8):390-394
[19]王德培,卡尔曼滤波在航空火控系统中的应用,西安,西北工业大学出版社,1985,121-152.
[20]宋文尧,张牙,卡尔曼滤波.北京.科学出版社, 1991,37-92
[21] C B Chang,R H Whiting,M Athans.On the state and parameter estimation for maneuvering re-entry vehicle,IEEE Transactions on Automatic Control,1977,22 (2):99-105
[22]程咏梅,潘泉,张洪才等,基于推广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
[23]李硕,曾涛,龙腾等,基于推广卡尔曼滤波的机载无源定位改进算法,北京理工大学学报, 2002,22(4):521-524
[24] E Mazor,A Averbuch,Y Bar-Shalom,Interacting Multiple Model Methods in Target Tracking:A Survey,IEEE Transactions on Aerospace and Electronic Systems(AES),1998,34(1):103-123
[25] Hammersley J M,Morton K W,Poor man’s Monte Carlo,Journal of the Royal Statistical Society,1954,16(1):23-38
[26] Handschin J E, Monte Carlo techniques for prediction and filtering of non-linear stochastic processes, Automatica, 1970, 6(3):555-563
[27] Y Bar-shalom, A G Jaffer,Adaptive nonlinear filtering for tracking with measurements of uncertain origin,Proceedings of the 11th IEEE Conference on Decision and Control,New Orleans,La,IEEE,1972:243-247
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