点源目标被动式跟踪算法研究
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摘要
作为目标跟踪领域的一个重要分支，被动式跟踪对于提高武器系统在现代电子战环境下的生存能力和作战能力有着重要的研究价值和应用前景。本文从被动定向跟踪和被动定位跟踪两个方面进行研究，主要工作成果如下：
1．简要概述被动定向跟踪，推导了球坐标系下的匀速运动目标的定向跟踪模型；状态建模的不完备性引起了模型噪声方差阵带有未知径距参数，因而需要解决参数在线辨识问题。本文使用两种方法进行参数辨识：1)从滤波残差正交性原理出发，推导出参数自适应辨识滤波器—PIBF；2)根据多模型的思想，对未知参数在参数空间进行量化，进而通过交互式多模型算法(IMM)的模型切换实现参数的在线辨识。此外，本文从工程角度出发，导出了两通道解耦的滤波模型，仿真分析表明此算法在保证跟踪性能的前提下降低了计算量。
2．被动定位跟踪不可避免地会遇到非线性问题。本文将典型的Monte Carlo非线性滤波器—Unscented Kalman Filter(UKF)、Particle Filter(PF)和UnscentedParticle Filter(UPF)应用于被动定位跟踪，并在二维空间目标跟踪仿真中与经典的扩展卡尔曼滤波算法(EKF)进行了对比；本文将UKF算法与概率数据关联算法(PDA)相结合，给出了适用于杂波环境下多站被动式跟踪的UKFPDA算法。
As an important branch of target tracking techniques, passive target tracking plays important roles in improving the viable ability and battle effectiveness of weapon systems in modern electronic warfares. This thesis focuses on the bearing-only target positioning and bearing-only target tracking problems .The main works are as follows:
The problem of bearing-only target positioning is first outlined. The dynamic model in the spherical coordinate is presented for bearing-only target positioning. However, such model has unknown covariance of process noise, which is related to the range between the target and the passive sensor. Here two parameter identification methods are proposed: 1) Based on the principle of the orthogonality among filter residuals, a parameter identification method is proposed, which results in Parameter-Identification Based Filter (PIBF). 2) Based on multiple model idea, the parameter is quantified in its value range and then adaptively identified via model switches. In addition, the decoupling two-channel filter model is also presented, which is shown much low computation burden and satisfactory accuracy in computer simulations.
The nonlinear problem is inevitable in bearing-only target tracking problem. Here the Monte Carlo based nonlinear filters are compared with the classical Extended Kalman Filter (EKF) in this field: the Unscented Kalman Filter (UKF), the Particle Filter (PF) and the Unscented Particle Filter (UPF). Furthermore, a multi-sensor passive tracking algorithm, through combining the UKF and Probability Data Association (PDA), is provided for bearing-only target tracking in dense clutters.
1．简要概述被动定向跟踪，推导了球坐标系下的匀速运动目标的定向跟踪模型；状态建模的不完备性引起了模型噪声方差阵带有未知径距参数，因而需要解决参数在线辨识问题。本文使用两种方法进行参数辨识：1)从滤波残差正交性原理出发，推导出参数自适应辨识滤波器—PIBF；2)根据多模型的思想，对未知参数在参数空间进行量化，进而通过交互式多模型算法(IMM)的模型切换实现参数的在线辨识。此外，本文从工程角度出发，导出了两通道解耦的滤波模型，仿真分析表明此算法在保证跟踪性能的前提下降低了计算量。
2．被动定位跟踪不可避免地会遇到非线性问题。本文将典型的Monte Carlo非线性滤波器—Unscented Kalman Filter(UKF)、Particle Filter(PF)和UnscentedParticle Filter(UPF)应用于被动定位跟踪，并在二维空间目标跟踪仿真中与经典的扩展卡尔曼滤波算法(EKF)进行了对比；本文将UKF算法与概率数据关联算法(PDA)相结合，给出了适用于杂波环境下多站被动式跟踪的UKFPDA算法。
As an important branch of target tracking techniques, passive target tracking plays important roles in improving the viable ability and battle effectiveness of weapon systems in modern electronic warfares. This thesis focuses on the bearing-only target positioning and bearing-only target tracking problems .The main works are as follows:
The problem of bearing-only target positioning is first outlined. The dynamic model in the spherical coordinate is presented for bearing-only target positioning. However, such model has unknown covariance of process noise, which is related to the range between the target and the passive sensor. Here two parameter identification methods are proposed: 1) Based on the principle of the orthogonality among filter residuals, a parameter identification method is proposed, which results in Parameter-Identification Based Filter (PIBF). 2) Based on multiple model idea, the parameter is quantified in its value range and then adaptively identified via model switches. In addition, the decoupling two-channel filter model is also presented, which is shown much low computation burden and satisfactory accuracy in computer simulations.
The nonlinear problem is inevitable in bearing-only target tracking problem. Here the Monte Carlo based nonlinear filters are compared with the classical Extended Kalman Filter (EKF) in this field: the Unscented Kalman Filter (UKF), the Particle Filter (PF) and the Unscented Particle Filter (UPF). Furthermore, a multi-sensor passive tracking algorithm, through combining the UKF and Probability Data Association (PDA), is provided for bearing-only target tracking in dense clutters.
引文
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[3] E. Fogel M. Gavish. Nth-order dynamics target observability from angle measurements. IEEE Transactions on Aerospace and Electronic Systems. 1988,24(2): 305～308
[4] C. Jaufffret D. Pillon. New observability criterion in target motion analysis. In Procceding of the NATO ASI on Underwater Acoustic Data Processing, Series E,Applied Sciences 161 Kingston, 1988, 479～484
[5] 潘泉，戴冠中，等．被动式跟踪客观观测性分析的非线性系统方法．信息与控制，1997，26(3)：168～174
[6] T.L.Song. Observability of target tracking with bearings-only measurements. IEEE Transactions on Aerospace and Electronic Systems, 1996,32(4): 1468～1471
[7] S. Blackman, R. Popoli. Design and Analysis of Modem Tracking System.Norwood MA: Artech House, 1999
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[9] Fawcett J.A. Effect of Course Maneuvers on Bearings-Only Range Estimation.IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.36, No.8,Aug 1988,1193～1199
[10] Moorman M. J. and T. E. Bullock. A Stochastic Perturbation Analysis o Bias in the Extended Kalman Filter as Applied to Bearings-Only Estimation. Proc.3 1st
Conf. on Decision and Control, Dec. 1992, 3778～3783
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[2] S. E. Hammel, V. J. Aidala. Observability requirements for Three-Dimensional Tracking via Angle Measurements. IEEE Transactions on Aerospace and Electronic Systems, 1985, 21(2): 200～207
[3] E. Fogel M. Gavish. Nth-order dynamics target observability from angle measurements. IEEE Transactions on Aerospace and Electronic Systems. 1988,24(2): 305～308
[4] C. Jaufffret D. Pillon. New observability criterion in target motion analysis. In Procceding of the NATO ASI on Underwater Acoustic Data Processing, Series E,Applied Sciences 161 Kingston, 1988, 479～484
[5] 潘泉，戴冠中，等．被动式跟踪客观观测性分析的非线性系统方法．信息与控制，1997，26(3)：168～174
[6] T.L.Song. Observability of target tracking with bearings-only measurements. IEEE Transactions on Aerospace and Electronic Systems, 1996,32(4): 1468～1471
[7] S. Blackman, R. Popoli. Design and Analysis of Modem Tracking System.Norwood MA: Artech House, 1999
[8] Van Huyssteen D. and M.Farooq. Performance Analysis of Bearings-Only Tracking Algorithm. Acquisition, Tracking and Pointing Ⅻ, Proc. SPIE,Vol.3365, April. 1998, 139～149
[9] Fawcett J.A. Effect of Course Maneuvers on Bearings-Only Range Estimation.IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.36, No.8,Aug 1988,1193～1199
[10] Moorman M. J. and T. E. Bullock. A Stochastic Perturbation Analysis o Bias in the Extended Kalman Filter as Applied to Bearings-Only Estimation. Proc.3 1st
Conf. on Decision and Control, Dec. 1992, 3778～3783
[11]Lindgren A. G. and K. F. Gong. Position and Velocity Estimation via Bearings Observations. IEEE Transactions on Aerospace and Electronic Systems. Vol. 14,July 1978, 564～577
[12]Aidala V. J. Kalman Filter Behavior in Bearings-Only Tracking Application.IEEE Transactions on Aerospace and Electronic Systems. Vol. 15, Jan. 1979,29～39
[13]Speyer J. L. and T. L. Song. A Comparison Between the Pseudomeasurement and Extended Kalman Observers. Proc.20th Conf. on Decision and Control. Dec. 1981,324～329
[14]Song T. L. and Speyer J. L. A Stochastic Analysis of Modified Gain Extended Kalman Filter with Applications to Estimation with Bearings-Only Measurements. IEEE Transactions on Automatic Control. Vol.30 Dec. 1985, 940～949
[15]Moorman M. J. and T. E. Bullock. A New Estimation for Passive Tracking of Maneuvering Targets'. Proc. 1st IEEE Conf. on Control Applications. Sept. 1992,1122～1127
[16]何启予．红外成像导引技术现状及发展趋势．红外与激光技术，1990．2．
[17]Moose R.L. An Adaptive State Estimation Solution to the Maneuvering Target Problem. IEEE Transactions on Automatic Control. 20, June. 1980.
[18]Torriri D.J. Statistical Theory of Passive Location System. IEEE Transactions on Aerospace and Electronic Systems.20, March 1984
[19]Petridis V. A. A Method of Bearing-only Velocity and Position Estimation. IEEE Transactions on Automatic Control. 26, April. 1981.
[20]Wesis H. Improved Extend Kalman Filter Design for Passive Tracking. IEEE Transactions on Automatic Control. Vol.25, Aug. 1980.
[21]Aidala V. J., S.E. Hammel. Utilization of Modified Polar Coordinates for Bearings-only Tracking. IEEE Transactions on Automatic Control. Vol.28,March. 1983.283～294
[22]Stallard D V. An Angle-Only Tracking Filter in Modified Spherical Coordinates[Z]. In: AIAA Guidance, Navigation and Control Conf, AIAA Paper 87-2380:
542-550
[23]Walter Grossman. Bearings-Only Tracking: A Hybrid Coordinates System Approach [J]. Journal of Guidance, Control, and Dynamics, 1994,17(3): 451-457
[24]孙仲康，周一宇，何黎星．单多基地有源无源定位技术北京：国防工业出版社，1996．5
[25]丁振，目标跟踪理论与算法．西安，西北工业大学博士学位论文，1995
[26]Johnson G..W. et al. Modified Polar Coordinates for Ranging from Doppler and Bearing Measurements. Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP-83 Boston, May 1983,907～910
[27]Passerieux J. M. et al. Target Motion Analysis with Bearing and Frequencies Measurement via Instrumental Variable Estimator. Proc. Int. Conf. On Acoustics,Speech and Signal Processing, ICASSP-89, Glasgow, Scotaland, May 1989,2645～2648
[28]Jauffret C. and Y. Bar-Shalom. Track Formation with Bearing and Frequency Measurements in clutter. IEEE Transactions on Aerospace and Electronics Systems, Vol.26, No.6, Nov. 1990,999～1009
[29]Le Cadre. and O.Tremois. Properties and Performance of Extended Target Motion Analysis. IEEE Transactions on Aerospace and Electronics Systems, Vol.32, No.1, Nov. 1996, 66～83
[30]Elser D.A. Time-to-Go (TTG) Determination in an IRST. Hughes Aircraft Co.Internal Memorandum Data, April 27,1984
[31]Jeffrey W. et al. Monocular Passive Ranging. Proc. Of IRIS Targets,Backgrounds and Discrimination, Feb. 1994, Vol. Ⅱ, 113～130
[32]Evans H.E. et al. The Theory of Single Sensor Altitude Determination. Proc. 1995 IEEE National Aerospace Conf., NAECON 1995, May 1995,851～858
[33]Sanderson R.B. Infrared Missile Warning Sensors. Proc. 1996 IEEE National Aerospace and Electronics Conf. NAECON 1996, May 1996,777～781
[34]Bar-Shalom Y. et al Multitarget-Multisensor Tracking: Principles and Techniques. Storrs, CT: YBS Publishing. 1995
[35]周宏仁，敬忠良，王培德．机动目标跟踪．北京：国防工业出版社，1991
[36]孙仲康，雷达数据处理．北京：国防工业出版社，1983
[37]H. W. Sorenson. Kalman Filtering Technique. Advances in Control System. Vol.3,1966
[38]Chang.C.B, Youens. L.C. An algorithm for Multitarget Tracking and Data Correlation. AD-A131313
[39]徐志弘．基于红外全方位警戒系统的多目标跟踪处理．舰船光学，1995(2)，19-26
[40]胡磊力，陈钰清，周海宪，邹盛怀．机载红外搜索跟踪系统及其信息处理技术的发展综述．应用光学，2000，S1，21-28
[41]王学伟，何友．舰载红外警戒系统多目标跟踪算法研究[J]．火力与指挥控制，1999，24(1)：38-42
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