预测滤波技术在光电目标跟踪中的应用研究
|
摘要
预测目标的运动参数实现复合控制是提高快速捕获和精密跟踪精度的重要手段。复合控制可以提高系统的无静差度,消除速度和加速度滞后误差,使跟踪精度提高几倍至十几倍。同时可以降低由于其它扰动引起的误差。
实现复合控制必须能够获得目标运动的角速度、角加速度信号,通过预测滤波技术可以得到空中目标运动的位置及其导数,因此,预测目标的运动参数对于提高精密跟踪精度具有十分重要的意义。
通常,目标的运动状态方程在直角坐标下描述,而传感器的测量值是在极坐标下取得的,因此,光电目标运动参数的滤波属于非线性滤波。本文研究高精度光电目标运动参数的非线性滤波。
首先,根据噪声的统计特性,建立了分别基于白噪声和有色噪声的直角坐标下的光电跟踪目标状态模型和分别基于极坐标系和直角坐标系的测量模型。
针对推广Kalman滤波算法精度低,易发散的缺点,认为线性化误差是影响跟踪精度的因素之一,为此,本文提出了实际测量噪声协方差的在线估计算法,该算法简单实用,计算量小。从而很好地解决了在实际应用中,测量噪声的时变协方差矩阵无法验前已知这一难题。同时在研究中发现目标的距离与线性化误差有关,而目标的角位置的非线性与线性化误差关系较小。
提出了改进的推广Kalman滤波算法:①采用混合坐标系,在直角坐标系用相对简单的状态方程描述目标的运动特性,目标轨迹外推,协方差矩阵的传播,增益的计算均在直角坐标下进行,而目标新息(残差)计算在极坐标下进行;②增加了线性化误差补偿。将距离的非线性通过附加噪声加以补偿,距离测量误差的方差每一步被重新估计。该算法有效的减小了极坐标测量值非线性的影响,跟踪性能优于推广Kalman滤波器。
为减少改进的推广Kalman滤波器的计算量,提出了贯序滤波方法(SMEKF)。采用贯序滤波算法,可以避免直接计算矩阵的逆,因此可以大大减少计算量。同时在研究中发现如果按照俯仰、方位、距离的顺序进行测量更新,可以提高估计精度。仿真研究表明,采用贯序滤波的改进的推广Kalman滤波算法不仅可以减小计算量,而且跟踪精度也大大提高。
提出了一种针对光电机动目标的修正机动加速度方差自适应算法。状态模型
Combined control is an important method for acquiring fast moving targets and improving tracking precision by prediction filtering parameters of target. The no difference grade of control system is increased by it. The lag errors of velocity and acceleration can also be removed. Combined control provides greatly superior performance than conventional control system. Furthermore, it can reduce some disturbances.Combined control must obtain the signals of target's velocity and acceleration by prediction filtering technology. So target's parameter estimation is important for tracking system.Usually a Cartesian coordinate frame is well suited to describe the target dynamics, in this description, the dynamics equations are often linear and uncoupled .Because measurements of the target location are expressed as nonlinear equations in Cartesian coordinates, the tracking problem is connected with nonlinear estimation. In this dissertation, applications of nonlinear filtering algorithms to target tracking are studied.At first, linear dynamic models are proposed based on white noise and colour noise. Observation models are built in Cartesian coordinates and Polar coordinates respectively.Extended kalman filter (EKF) is simple and practical. But the EKF has the disadvantage of model linearization error. This filer is always divergence. The author analyzed the relationship between tracking accuracy and linearization error and presents an algorithm that can estimate the statistic properties of measurements noise, it also compensates for the errors of model linearization to overcome this disadvantage. It was founded nonlinearities of the range measurements influence on the tracking accuracy. But nonlinearities of two angle measurements do not appear significant.A new filtering algorithm for improving tracking performance is developed in mixed coordinate system which includes correct evaluation of the measurement error covariance. The target dynamics in inertial rectangular coordinate is modeled. The
estimation of target's trajectory and calculation of gain are performed in it. While residual is calculated in polar coordinates. The compensation of linearization error is included in MEKF. The range measurement nonlinearity is treated as additional measurement error. The measurement error variance for the range is calculated adaptively at each time. The algorithm reduces nonlinearity effect. So the performance of this filter is better than EKF.In order to reduce computational complexity, it is preferable to sequentially process scalar component of the measurement vector instead of processing it as a single data. The advantage of sequential processing is that instead of requiring the matrix inversion, which leads to considerable computational savings. Furthermore, in the case of nonlinear measurements, sequential processing of the measurement vector in particular order may produce additional benefit of improving estimation accuracy. .Simulation results show that the proposed method offers superior performance.An improved adaptive filtering algorithm based on current statistical model is presented by using the relationship between maneuvering acceleration and its covariance. The acceleration of a maneuvering target is considered as a time-correlation random process with non-zero mean values. The prediction of acceleration is as mean value of maneuvering acceleration and its covariance varies adaptively. The simulation results show that adaptive algorithm can estimate the position, velocity and acceleration of target and require less computation, no matter the target is maneuvering at any way.In the end, the algorithms discussed in this dissertation are tested in experiments for different targets and input noises. The results verify the proposed algorithms.
实现复合控制必须能够获得目标运动的角速度、角加速度信号,通过预测滤波技术可以得到空中目标运动的位置及其导数,因此,预测目标的运动参数对于提高精密跟踪精度具有十分重要的意义。
通常,目标的运动状态方程在直角坐标下描述,而传感器的测量值是在极坐标下取得的,因此,光电目标运动参数的滤波属于非线性滤波。本文研究高精度光电目标运动参数的非线性滤波。
首先,根据噪声的统计特性,建立了分别基于白噪声和有色噪声的直角坐标下的光电跟踪目标状态模型和分别基于极坐标系和直角坐标系的测量模型。
针对推广Kalman滤波算法精度低,易发散的缺点,认为线性化误差是影响跟踪精度的因素之一,为此,本文提出了实际测量噪声协方差的在线估计算法,该算法简单实用,计算量小。从而很好地解决了在实际应用中,测量噪声的时变协方差矩阵无法验前已知这一难题。同时在研究中发现目标的距离与线性化误差有关,而目标的角位置的非线性与线性化误差关系较小。
提出了改进的推广Kalman滤波算法:①采用混合坐标系,在直角坐标系用相对简单的状态方程描述目标的运动特性,目标轨迹外推,协方差矩阵的传播,增益的计算均在直角坐标下进行,而目标新息(残差)计算在极坐标下进行;②增加了线性化误差补偿。将距离的非线性通过附加噪声加以补偿,距离测量误差的方差每一步被重新估计。该算法有效的减小了极坐标测量值非线性的影响,跟踪性能优于推广Kalman滤波器。
为减少改进的推广Kalman滤波器的计算量,提出了贯序滤波方法(SMEKF)。采用贯序滤波算法,可以避免直接计算矩阵的逆,因此可以大大减少计算量。同时在研究中发现如果按照俯仰、方位、距离的顺序进行测量更新,可以提高估计精度。仿真研究表明,采用贯序滤波的改进的推广Kalman滤波算法不仅可以减小计算量,而且跟踪精度也大大提高。
提出了一种针对光电机动目标的修正机动加速度方差自适应算法。状态模型
Combined control is an important method for acquiring fast moving targets and improving tracking precision by prediction filtering parameters of target. The no difference grade of control system is increased by it. The lag errors of velocity and acceleration can also be removed. Combined control provides greatly superior performance than conventional control system. Furthermore, it can reduce some disturbances.Combined control must obtain the signals of target's velocity and acceleration by prediction filtering technology. So target's parameter estimation is important for tracking system.Usually a Cartesian coordinate frame is well suited to describe the target dynamics, in this description, the dynamics equations are often linear and uncoupled .Because measurements of the target location are expressed as nonlinear equations in Cartesian coordinates, the tracking problem is connected with nonlinear estimation. In this dissertation, applications of nonlinear filtering algorithms to target tracking are studied.At first, linear dynamic models are proposed based on white noise and colour noise. Observation models are built in Cartesian coordinates and Polar coordinates respectively.Extended kalman filter (EKF) is simple and practical. But the EKF has the disadvantage of model linearization error. This filer is always divergence. The author analyzed the relationship between tracking accuracy and linearization error and presents an algorithm that can estimate the statistic properties of measurements noise, it also compensates for the errors of model linearization to overcome this disadvantage. It was founded nonlinearities of the range measurements influence on the tracking accuracy. But nonlinearities of two angle measurements do not appear significant.A new filtering algorithm for improving tracking performance is developed in mixed coordinate system which includes correct evaluation of the measurement error covariance. The target dynamics in inertial rectangular coordinate is modeled. The
estimation of target's trajectory and calculation of gain are performed in it. While residual is calculated in polar coordinates. The compensation of linearization error is included in MEKF. The range measurement nonlinearity is treated as additional measurement error. The measurement error variance for the range is calculated adaptively at each time. The algorithm reduces nonlinearity effect. So the performance of this filter is better than EKF.In order to reduce computational complexity, it is preferable to sequentially process scalar component of the measurement vector instead of processing it as a single data. The advantage of sequential processing is that instead of requiring the matrix inversion, which leads to considerable computational savings. Furthermore, in the case of nonlinear measurements, sequential processing of the measurement vector in particular order may produce additional benefit of improving estimation accuracy. .Simulation results show that the proposed method offers superior performance.An improved adaptive filtering algorithm based on current statistical model is presented by using the relationship between maneuvering acceleration and its covariance. The acceleration of a maneuvering target is considered as a time-correlation random process with non-zero mean values. The prediction of acceleration is as mean value of maneuvering acceleration and its covariance varies adaptively. The simulation results show that adaptive algorithm can estimate the position, velocity and acceleration of target and require less computation, no matter the target is maneuvering at any way.In the end, the algorithms discussed in this dissertation are tested in experiments for different targets and input noises. The results verify the proposed algorithms.
引文
[1] 马佳光.复合控制及等效复合控制原理及应用[J].光学工程.1988,15(5):1-16.
[2] Jazwinski, A.H. Stochastic Processes and Filtering Theory[M]. Academic Press, New York, 1979: 1-70.
[3] 马佳光.捕获跟踪与瞄准系统的基本技术问题[J].光学工程.1989.3:1-42.
[4] 宋文尧,张牙.卡尔曼滤波[M].北京:科学出版社,1991:135-159.
[5] 贾沛璋,朱征桃.最优估计及其应用[M].北京:科学出版社,1984.
[6] Jazwinski AH. Adaptive filtering[J]. Automatica. 1969, 5: 475-485.
[7] Chin L. Advances in adative filtering. In: Advances in control and dynamic systems[M].New York, Academic Press, Inc. 1979.
[8] Chang C B, Whiting R H, Athans M. On the state and parameter estimation for maneuvering re-entry vehicle[J]. IEEE Transaction on automatic control. 1977, 22(2): 99-105.
[9] Benedic T R, Bordner G W. Synthesis of radar track-while-scan smoothing equations[J]. IEEE Transactions on Automatic Control. 1962, 7(4): 27-32.
[10] Simpon H R. Performance measures and optimization conditions for third-order sampled data tracker[J]. IEEE Transactions on Automatic Control. 1963, 8(2): 182-183.
[11] Kalata P R. The tracking index: A generalized parameter for α-β, α-β-γ target trackers[J]. IEEE Transactions on Aerospace and Electronic Systems. 1984, 20(2): 174-182.
[12] Fitzgerald R J. Simple tracking filter: steady state filtering and smooth performance[J] IEEE Transactions on Aerospace and Electronic Systems. 1980, 16(6): 860-864.
[13] Michel Verhaegen, Paul, Van Dooren. Numerical aspects of different kalman filter. IEEE Transaction on Automatic Control. 1986, 31(10): 907-917.
[14] Gordan, N.J, Salmond D.J, and Smith A.F.M.. A Novel Approach to Non-Linear/Non-Gaussian Bayesian State Estimation[J]. IEE Proceeding Pt.F. 1993, 140(2): 107-113.
[15] 金耀初,诸静,蒋静坪.神经网络自学习模糊控制及其应用[J].电工技术学报.1994,(4):35-39.
[16] Daum, f.e. Exact Finite Dimensional Nonlinear Filers[J]. IEEE Transactions on Automatic Control. 1986, 31(7): 616-621.
[17] 杨春玲,刘国岁.给予转换坐标卡尔曼滤波算法的雷达目标跟踪[J].现代雷达.1998,5(20):48-54.
[18] Don Lerro, Yaakov Bar-Shalom. Tracking with debased consistent converted measurements versus EKF[J]. IEEE Transactions on Aerospace and Electroninc Systems. 1993, 129(3): 1015-1021.
[19] B.Kosko. Neural Networks and Fuzzy System[M]. Prentice Hall, 1992.
[20] 吴慈伶.机载火控雷达TWS滤波算法仿真研究[J].现代雷达.2002,23(1):33-38.
[21] 徐洪奎,王东进.基于卡尔曼滤波的组网雷达系统目标跟踪分析[J].系统工程与电子技术.2001,23(11):67-69.
[22] 马建新.Kalman滤波在遥测伺服跟踪系统的应用[J].导弹与航天运载技术.1994,4:60-65.
[23] 王学伟,何友.机载IRST跟踪算法[J].光电工程.1999,26(4):63-68.
[24] 潘丽娜.红外警戒系统中数据互联算法[J].红外与激光工程.1998,27(6):5-7.
[25] 欧阳荣彪译.关于高性能导弹实时视频跟踪的预测和滤波技术的评价.NAECON78: Proceedings of the National Aerospace and Electronics Conference Dayton Ohis. 1978, (2): 897-904.
[26] 张友民,张洪才,带戴冠中等.非线性滤波方法及其在飞行状态及参数估计中的应用[J].航空学报.1994,15(5):620-626.
[27] Schulthess, Lt. M. er al. Altitude control and trajectory for the High Balloon Experiment[J]. SPIE. Acquisition, Tracking and Pointing Ⅷ1994, 2221: 590-609.
[28] 孙仲康.雷达数据数字处理[M].北京:国防工业出版社,1983.
[29] A.费利那,F.A.斯塔德.雷达数据处理[M].第一卷.北京:国防工业出版社,1988.
[30] 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.
[31] B.D.O.安德森.J.B.摩尔.最佳滤波[M].北京:国防工业出版社,1983.
[32] Kalman, R.E. A new Approach to Linear Filtering and Prediction Problems[J]. Trans. ASME. Jour. of Basic Engineering. 1960, 82(2): 34-45.
[33] Ramachandra K V. Analytical results for a Kalman tracker using position and rate measurements. IEEE Transactions on Aerospace and Electronic Systems. 1983, 19(5): 776-778.
[34] Kerner A.J., Kalman-Bucy and Minmax. Filtering[J]. IEEE Transaction on Automatic Control. 1980, 25(2): 291-292.
[35] CG, Wang J., Shieh L.S. Interval Kalman Filtering[J]. IEEE Transaction on Aerospace and Electronic Systems. 1997, 33(1): 232-240.
[36] Hashirao M., Kawase T., Sasase I.. A Variable γ H_∞ Filter for A Maneuvering Target Tracking Using Acceleration Estimate[J]. IEEE International Rader Conference. 2000: 76-80.
[37] Bar-Shalom Y, and Fortmann T.E.. Tracking and Data Association[M]. New York: Academic Press, 1988.
[38] Bar-Shalom, Y.and Li, X.. Estimation and Tracking: Principle, Techniques and Tracking: Principle, Techniques and Software[M]. Norwood. MA: Artech House.1993.
[39] Singer, R.A.. Estimating optimal tracking filter Performance for Manned Maneuvering Targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 1970, 5(4): 473-483.
[40] Bar-Shalom Y., Birmiwal,K.. "Variable dimension filter for maneuvering Target Tracking[J]. IEEE Transactions on Aerospace and Electronic Systems. 1982, 18(5): 611-619.
[41] Thorp. J.S.. Optimal Tracking of Maneuvering Targets[J]. IEEE Transactions on Aerospace and Electronic Systems. 1973, 9(4): 512-519.
[42] Magile,D.T.. Optimal Adaptive Estimation and of Sampled Stochastic Processes[J]. IEEE Transactions on Automatic Control. 1965, 10(5): 434-439.
[43] Chan, Y.T., Hu,A.G.C. and Plant,J.B.. A Kalman Filter Based Tracking Scheme with Input Estimation[J]. .IEEE Transactions on Aerospace and Electronic Systems. 1979, 15(2): 237-244.
[44] Daum,F.E.,and Fitzgerald,R.J. Decoupled Kalman filters for phased array radar tracking[J]. IEEE Transaction on Automatic Control. 1983, 28(3): 269-283.
[45] Chang,C.B, Tabaczynski,J.A. Application of state estimation to target tracking[J]. IEEE Transactions on Automatic Control. 1984, 29(2): 98-109.
[46] Whang,I.H., Sung,T.,Lee,J.G. Stability of a decoupled tracking filter with pseudo measurements in line-of-sight Cartesian co-ordinate system[J] . IEE Proceedings-Radar, Sonar Navigation. 141.1.1994.2-8.
[47] Whang,I.H.,Lee,J.G.Sung,T. Modified input estimation technique using Pseudoresiduals[J] . IEEE Transactions on Aerospace and Electronic Systems. 1994, 30(1): 220-228.
[48] Park,Y.H., Seo,J.H., Lee,J.G. Tracking using the variable-dimension filter with input estimation[J] . IEEE Transactions on Aerospace and Electronic Systems. 1995, 31(1): 399-408.
[49] Sung,T , and Lee,J.G.A. Decoupled adaptive tracking filter for real applications[J]. IEEE Transactions on Aerospace and Electronic Systems. 1997, 33(3): 1025-1030.
[50] Mehra,R.K. A comparison of several nonlinear filters for reentry vehicle tracking[J]. IEEE Transactions on Automatic Control. 1971, 16(4): 307-319.
[51] Cloutier,J.R.Evers,J,H.,and Feeley,J.J. Assessment of air-to-air missile guidance and control technology[J]. IEEE Control System Magazine. 1989, 9(10): 27-34.
[52] Spingarm,K.,and Weidemann,H.L. Linear regression filtering and prediction for target tracking maneuvering aircraft targets[J]. IEEE Transactions on Aerospace and Electronic Systems. 1972, 8(6): 800-810.
[53] D.Lee and Y.Bar-shalom. Tracking with debased consistent converted measurements versus EKF[J]. IEEE Transactions on Aerospace and Electronic Systems. 1993, 29(3): 1015-1022.
[54] SEONG-Taek Park, JANG Gyu Lee. Improved Kalman filter design fir three-dimensional radar measurements[J]. IEEE Transaction on Aerospace and Electronic System. 2001, 37(2): 727-739.
[55] 王建立.光电经纬仪电视跟踪伺服系统捕获跟踪快速运动目标研究[D].中国科学院长春光学精密机械与物理研究所博士论文,2002.
[56] 黄永梅,马佳光,傅承毓.预测滤波技术在光电经纬仪中的应用[J].光电工程.2002,4(29):5-9.
[57] Kenneth S.Miller, Donald M. Leskiw. Nonlinear estimation with Radar observation[J]. IEEE Transactions on Aerospace Electronic Systems. 1982, 18(2): 192-198.
[58] R.A.Singer. Estimating optimal tracking filter performance for manned maneuvering targets[J]. IEEE Transactions on Aerospace Electronic System. 1970, 6(4): 473-483.
[59] R.L.Moose. An adaptive state estimation solution to the maneuvering target problem[J]. IEEE Transactions on Automatic Control. 1975, 20(6): 359-362.
[60] N.H.Gholson and R.L.Moose. Maneuvering target tracking using adaptive state estimation [J]. IEEE Transactions on Aerospace Electronic Systems. 1977, 13(3): 310-315.
[61] R.L.Moose et al. Modeling and estimation for tracking maneuvering targets[J]. IEEE Transactions on Aerospace Electronic Systems. 1979, 15(3): 448-456.
[62] Y.T.Chan, et al. A Kalman filter based tracking scheme with in put estimation[J]. IEEE Transactions on Aerospace Electronic Systems. 1979, 15(2) : 237-244.
[63] Y Bar-Shalom, K C Chang, H A P Blom. Tracking a maneuvering target using input estimation versus interacting multiple model algorithms[J]. IEEE Transactions on Aerospace Electronic Systems. 1989, 16(2): 296-300.
[64] 敬忠良,徐宏,周雪琴等.基于神经网络的机动目标信息融合与并行自适应跟踪[J].航空学报.1995,16(6):12-13.
[65] 范凯,陶然,周思永.模糊数据融合在目标跟踪中的应用[J].北京理工大学学报.2000,20(3):343-346.
[66] J.JS.Thorp. Optimal tracking of maneuvering targets[J]. IEEE Transactions on Aerospace Electronic Systems. 1973, 9(4): 512-519.
[67] 王培德,陶涛.机动目标“全面”自适应跟踪滤波算法[J].中国航空学会自控年会论文集,1987.
[68] 蔡庆宇,薛毅,张伯彦.相控阵雷达数据处理及其仿真技术[M].北京:国防工业出版社,1997.
[69] Allen R.R, Blackman S.S. Implementation of an Angle-only Tracking Filter[J]. SPIE, Signal and Data Processing of Small Target. 1991, 1481: 292-303.
[70] Stallard D V. An. Angle-only Tracking Filtering in Modified Spherical Coordinates[Z]. In: AIAA Guidance, Navigation and Control Conf, AIAA Paper 87-2380: 542-550.
[71] Alspack D L, Serenson H.W. Nonlinear bayesian estimation using Gaussian sums[J]. IEEE Transactions on Automatic Control. 1972, 17(9): 439-448.
[72] S.C.Kramer and K.D.and H.W.Sprenson. Recursive bayesian estimation using Piecewise Constant Approximation[J]. Automation. 1988, 24(6): 789-801.
[73] 张有为,立少洪编著.雷达系统分析[M].北京:国防工业出版社,1981.
[74] Sorenson H.W.. Kalman filtering Theory and Applications[M]. IEEE Press, 1985.
[75] 王晓华,敬忠良,陈非等.红外与激光主/被动联合跟踪算法[J].红外与激光工程.2000,30(4):298-300.
[76] 周荻.非线性滤波理论在制导和跟踪中的应用[D].哈尔滨工业大学博士论文,1996.
[77] 邓自立,王建国.带模型误差系统自适应Kalman滤波的虚拟噪声补偿技术[J].信息与控制.1988,17(5):1-5.
[78] Nahi, N.E. Optimal Recursive Estimation with Uncertain observation[J]. IEEE Transactions on Information Theory. 1969, 15(2): 475-480.
[79] Bar-shalom Y., Birmiwal K.. Variable dimension filter for maneuvering target tracking[J]. IEEE Transaction Aerospace Electronic Systems. 1982, 18(5): 611-619.
[80] Thorp J.S.. Optimal tracking of maneuvering targets[J]. IEEE Transactions on Aerospace Electronic System. 1973, 9(2): 512-519.
[81] Chan Y.T, Plant J.B., Botlomley J.R.T.. A Kalman tracking with a simple input estimator[J]. IEEE Transactions Electronic Systems. 1982, 18(2): 235-241.
[83] R.J. Fitzgerald. On reentry vehicle tracking in various coordinate system[J]. IEEE Transactions on Automatic Control. 1974, 19: 581-582.
[84] M.L.Andrade Netto.L.Gimeno, M.J.Mendes. On the optimal and suboptimal nonlinear filtering problem for discrete-time system[J]. IEEE Transactions on Automatic Control. 1978, 23: 1062-1067.
[85] 李树英.许茂增.随机系统的滤波与控制.北京:国防工业出版社,1991.
[86] 邓自立,王建国.非线性系统的自适应Kalman滤波[J].自动化学报.1987,13(5):375-379.
[87] 马佳光.预测目标角速度的最小平方滤波器[J].光学工程.1988,5:46-53.
[88] John Murray Fitts. Aided tracking as applied to high accuracy pointing system[J]. IEEE Transactions on Aerospace and Electronic Systems 1973, 9(3): 350-365.
[89] Robert A.Singer, Kenneth W. Behnke. Real-time tracking Filter evaluation and selecations for Tactical Application[J]. IEEE Transactions on Aerospace and Electronic Systems. 1971, 7(1): 100-106.
[90] 杨秀华,吉桐伯,陈涛,王延风.预测滤波技术在电视跟踪系统的应用[J].吉林大学学报(信息科学版),2003,21(4):247-250.
[91] YANG Xiu-hua, JI Tong-bo, CHEN Tao, Hart Xiao-quan, Wang Yan-feng, Zhao Yan. Application of prediction filtering technique in photoelectric tracking system[A]. 5th International Symposium on Test and Measurement(ISTM'2003), Vol.4: 2762-2764. (EI收录)
[92] 杨秀华,吉桐伯,陈涛,陈娟.测滤波技术在光电跟踪系统的应用[J].光与控制,2003,10(3):11-15.
[93] 杨秀华,吉桐伯,陈涛.卡尔曼滤波器在光电经纬仪的应用[J].测试技术学报,2003,17(4):324-328.(EI检索)
[94] 杨秀华,刘岩.系统分析工具GOMTOOL研究[J].光学精密工程.2000,8(3):278-282.
[95] 吉桐伯,陈娟,杨秀华,陈涛.地平式光电望远镜天顶盲区影响因素[J].光学精密工程.2003,11(3):296-300.
[96] JI Tong-bo, YANG Xiu-hua, CHEN Juan, CHEN Tao. Altitude-azimuth tracking performance and method around the zenith[A]. 5th International Symposium on Test and Measurement (ISTM'2003), Vol. 5: 3731-3734. (EI收录)
[97] 吉桐伯,杨秀华,陈涛,王建立,陈娟.地平式跟踪架近天顶跟踪性能影响因素分析(英文)[J].光电子激光.2004,(3).(EI检索)
[98] JI Tong-bo, YANG Xiu-hua, CHEN Juan, CHEN Tao. Analysis of tracking performance for altitude-azimuth pedestal near the zenith[A]. 5th International Symposium on Instrumentation and Control Technology (ISICT'2003), SPIE Vol.5253: 398-401. (EI检索)
[99] 范影乐,杨胜天,李轶等.MATLAB仿真应用详解[M].北京:人民邮电出版社,2001.
[100] 欧阳黎明.MATLAB控制系统设计[M].北京:国防工业出版社,2001.
[101] SEONG-Taek Park, JANG Gyu Lee. Design of a practical tracking algorithm with radar measurements[J]. IEEE Transactions on Aerospace and Electronic Systems. 1998, 34(4): 1337-1343.
[102] 王宏强,黎湘,刘丹.非线性系统中的机动目标跟踪算法[J].国防科技大学学报.2002,24(4):57-58.
[2] Jazwinski, A.H. Stochastic Processes and Filtering Theory[M]. Academic Press, New York, 1979: 1-70.
[3] 马佳光.捕获跟踪与瞄准系统的基本技术问题[J].光学工程.1989.3:1-42.
[4] 宋文尧,张牙.卡尔曼滤波[M].北京:科学出版社,1991:135-159.
[5] 贾沛璋,朱征桃.最优估计及其应用[M].北京:科学出版社,1984.
[6] Jazwinski AH. Adaptive filtering[J]. Automatica. 1969, 5: 475-485.
[7] Chin L. Advances in adative filtering. In: Advances in control and dynamic systems[M].New York, Academic Press, Inc. 1979.
[8] Chang C B, Whiting R H, Athans M. On the state and parameter estimation for maneuvering re-entry vehicle[J]. IEEE Transaction on automatic control. 1977, 22(2): 99-105.
[9] Benedic T R, Bordner G W. Synthesis of radar track-while-scan smoothing equations[J]. IEEE Transactions on Automatic Control. 1962, 7(4): 27-32.
[10] Simpon H R. Performance measures and optimization conditions for third-order sampled data tracker[J]. IEEE Transactions on Automatic Control. 1963, 8(2): 182-183.
[11] Kalata P R. The tracking index: A generalized parameter for α-β, α-β-γ target trackers[J]. IEEE Transactions on Aerospace and Electronic Systems. 1984, 20(2): 174-182.
[12] Fitzgerald R J. Simple tracking filter: steady state filtering and smooth performance[J] IEEE Transactions on Aerospace and Electronic Systems. 1980, 16(6): 860-864.
[13] Michel Verhaegen, Paul, Van Dooren. Numerical aspects of different kalman filter. IEEE Transaction on Automatic Control. 1986, 31(10): 907-917.
[14] Gordan, N.J, Salmond D.J, and Smith A.F.M.. A Novel Approach to Non-Linear/Non-Gaussian Bayesian State Estimation[J]. IEE Proceeding Pt.F. 1993, 140(2): 107-113.
[15] 金耀初,诸静,蒋静坪.神经网络自学习模糊控制及其应用[J].电工技术学报.1994,(4):35-39.
[16] Daum, f.e. Exact Finite Dimensional Nonlinear Filers[J]. IEEE Transactions on Automatic Control. 1986, 31(7): 616-621.
[17] 杨春玲,刘国岁.给予转换坐标卡尔曼滤波算法的雷达目标跟踪[J].现代雷达.1998,5(20):48-54.
[18] Don Lerro, Yaakov Bar-Shalom. Tracking with debased consistent converted measurements versus EKF[J]. IEEE Transactions on Aerospace and Electroninc Systems. 1993, 129(3): 1015-1021.
[19] B.Kosko. Neural Networks and Fuzzy System[M]. Prentice Hall, 1992.
[20] 吴慈伶.机载火控雷达TWS滤波算法仿真研究[J].现代雷达.2002,23(1):33-38.
[21] 徐洪奎,王东进.基于卡尔曼滤波的组网雷达系统目标跟踪分析[J].系统工程与电子技术.2001,23(11):67-69.
[22] 马建新.Kalman滤波在遥测伺服跟踪系统的应用[J].导弹与航天运载技术.1994,4:60-65.
[23] 王学伟,何友.机载IRST跟踪算法[J].光电工程.1999,26(4):63-68.
[24] 潘丽娜.红外警戒系统中数据互联算法[J].红外与激光工程.1998,27(6):5-7.
[25] 欧阳荣彪译.关于高性能导弹实时视频跟踪的预测和滤波技术的评价.NAECON78: Proceedings of the National Aerospace and Electronics Conference Dayton Ohis. 1978, (2): 897-904.
[26] 张友民,张洪才,带戴冠中等.非线性滤波方法及其在飞行状态及参数估计中的应用[J].航空学报.1994,15(5):620-626.
[27] Schulthess, Lt. M. er al. Altitude control and trajectory for the High Balloon Experiment[J]. SPIE. Acquisition, Tracking and Pointing Ⅷ1994, 2221: 590-609.
[28] 孙仲康.雷达数据数字处理[M].北京:国防工业出版社,1983.
[29] A.费利那,F.A.斯塔德.雷达数据处理[M].第一卷.北京:国防工业出版社,1988.
[30] 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.
[31] B.D.O.安德森.J.B.摩尔.最佳滤波[M].北京:国防工业出版社,1983.
[32] Kalman, R.E. A new Approach to Linear Filtering and Prediction Problems[J]. Trans. ASME. Jour. of Basic Engineering. 1960, 82(2): 34-45.
[33] Ramachandra K V. Analytical results for a Kalman tracker using position and rate measurements. IEEE Transactions on Aerospace and Electronic Systems. 1983, 19(5): 776-778.
[34] Kerner A.J., Kalman-Bucy and Minmax. Filtering[J]. IEEE Transaction on Automatic Control. 1980, 25(2): 291-292.
[35] CG, Wang J., Shieh L.S. Interval Kalman Filtering[J]. IEEE Transaction on Aerospace and Electronic Systems. 1997, 33(1): 232-240.
[36] Hashirao M., Kawase T., Sasase I.. A Variable γ H_∞ Filter for A Maneuvering Target Tracking Using Acceleration Estimate[J]. IEEE International Rader Conference. 2000: 76-80.
[37] Bar-Shalom Y, and Fortmann T.E.. Tracking and Data Association[M]. New York: Academic Press, 1988.
[38] Bar-Shalom, Y.and Li, X.. Estimation and Tracking: Principle, Techniques and Tracking: Principle, Techniques and Software[M]. Norwood. MA: Artech House.1993.
[39] Singer, R.A.. Estimating optimal tracking filter Performance for Manned Maneuvering Targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 1970, 5(4): 473-483.
[40] Bar-Shalom Y., Birmiwal,K.. "Variable dimension filter for maneuvering Target Tracking[J]. IEEE Transactions on Aerospace and Electronic Systems. 1982, 18(5): 611-619.
[41] Thorp. J.S.. Optimal Tracking of Maneuvering Targets[J]. IEEE Transactions on Aerospace and Electronic Systems. 1973, 9(4): 512-519.
[42] Magile,D.T.. Optimal Adaptive Estimation and of Sampled Stochastic Processes[J]. IEEE Transactions on Automatic Control. 1965, 10(5): 434-439.
[43] Chan, Y.T., Hu,A.G.C. and Plant,J.B.. A Kalman Filter Based Tracking Scheme with Input Estimation[J]. .IEEE Transactions on Aerospace and Electronic Systems. 1979, 15(2): 237-244.
[44] Daum,F.E.,and Fitzgerald,R.J. Decoupled Kalman filters for phased array radar tracking[J]. IEEE Transaction on Automatic Control. 1983, 28(3): 269-283.
[45] Chang,C.B, Tabaczynski,J.A. Application of state estimation to target tracking[J]. IEEE Transactions on Automatic Control. 1984, 29(2): 98-109.
[46] Whang,I.H., Sung,T.,Lee,J.G. Stability of a decoupled tracking filter with pseudo measurements in line-of-sight Cartesian co-ordinate system[J] . IEE Proceedings-Radar, Sonar Navigation. 141.1.1994.2-8.
[47] Whang,I.H.,Lee,J.G.Sung,T. Modified input estimation technique using Pseudoresiduals[J] . IEEE Transactions on Aerospace and Electronic Systems. 1994, 30(1): 220-228.
[48] Park,Y.H., Seo,J.H., Lee,J.G. Tracking using the variable-dimension filter with input estimation[J] . IEEE Transactions on Aerospace and Electronic Systems. 1995, 31(1): 399-408.
[49] Sung,T , and Lee,J.G.A. Decoupled adaptive tracking filter for real applications[J]. IEEE Transactions on Aerospace and Electronic Systems. 1997, 33(3): 1025-1030.
[50] Mehra,R.K. A comparison of several nonlinear filters for reentry vehicle tracking[J]. IEEE Transactions on Automatic Control. 1971, 16(4): 307-319.
[51] Cloutier,J.R.Evers,J,H.,and Feeley,J.J. Assessment of air-to-air missile guidance and control technology[J]. IEEE Control System Magazine. 1989, 9(10): 27-34.
[52] Spingarm,K.,and Weidemann,H.L. Linear regression filtering and prediction for target tracking maneuvering aircraft targets[J]. IEEE Transactions on Aerospace and Electronic Systems. 1972, 8(6): 800-810.
[53] D.Lee and Y.Bar-shalom. Tracking with debased consistent converted measurements versus EKF[J]. IEEE Transactions on Aerospace and Electronic Systems. 1993, 29(3): 1015-1022.
[54] SEONG-Taek Park, JANG Gyu Lee. Improved Kalman filter design fir three-dimensional radar measurements[J]. IEEE Transaction on Aerospace and Electronic System. 2001, 37(2): 727-739.
[55] 王建立.光电经纬仪电视跟踪伺服系统捕获跟踪快速运动目标研究[D].中国科学院长春光学精密机械与物理研究所博士论文,2002.
[56] 黄永梅,马佳光,傅承毓.预测滤波技术在光电经纬仪中的应用[J].光电工程.2002,4(29):5-9.
[57] Kenneth S.Miller, Donald M. Leskiw. Nonlinear estimation with Radar observation[J]. IEEE Transactions on Aerospace Electronic Systems. 1982, 18(2): 192-198.
[58] R.A.Singer. Estimating optimal tracking filter performance for manned maneuvering targets[J]. IEEE Transactions on Aerospace Electronic System. 1970, 6(4): 473-483.
[59] R.L.Moose. An adaptive state estimation solution to the maneuvering target problem[J]. IEEE Transactions on Automatic Control. 1975, 20(6): 359-362.
[60] N.H.Gholson and R.L.Moose. Maneuvering target tracking using adaptive state estimation [J]. IEEE Transactions on Aerospace Electronic Systems. 1977, 13(3): 310-315.
[61] R.L.Moose et al. Modeling and estimation for tracking maneuvering targets[J]. IEEE Transactions on Aerospace Electronic Systems. 1979, 15(3): 448-456.
[62] Y.T.Chan, et al. A Kalman filter based tracking scheme with in put estimation[J]. IEEE Transactions on Aerospace Electronic Systems. 1979, 15(2) : 237-244.
[63] Y Bar-Shalom, K C Chang, H A P Blom. Tracking a maneuvering target using input estimation versus interacting multiple model algorithms[J]. IEEE Transactions on Aerospace Electronic Systems. 1989, 16(2): 296-300.
[64] 敬忠良,徐宏,周雪琴等.基于神经网络的机动目标信息融合与并行自适应跟踪[J].航空学报.1995,16(6):12-13.
[65] 范凯,陶然,周思永.模糊数据融合在目标跟踪中的应用[J].北京理工大学学报.2000,20(3):343-346.
[66] J.JS.Thorp. Optimal tracking of maneuvering targets[J]. IEEE Transactions on Aerospace Electronic Systems. 1973, 9(4): 512-519.
[67] 王培德,陶涛.机动目标“全面”自适应跟踪滤波算法[J].中国航空学会自控年会论文集,1987.
[68] 蔡庆宇,薛毅,张伯彦.相控阵雷达数据处理及其仿真技术[M].北京:国防工业出版社,1997.
[69] Allen R.R, Blackman S.S. Implementation of an Angle-only Tracking Filter[J]. SPIE, Signal and Data Processing of Small Target. 1991, 1481: 292-303.
[70] Stallard D V. An. Angle-only Tracking Filtering in Modified Spherical Coordinates[Z]. In: AIAA Guidance, Navigation and Control Conf, AIAA Paper 87-2380: 542-550.
[71] Alspack D L, Serenson H.W. Nonlinear bayesian estimation using Gaussian sums[J]. IEEE Transactions on Automatic Control. 1972, 17(9): 439-448.
[72] S.C.Kramer and K.D.and H.W.Sprenson. Recursive bayesian estimation using Piecewise Constant Approximation[J]. Automation. 1988, 24(6): 789-801.
[73] 张有为,立少洪编著.雷达系统分析[M].北京:国防工业出版社,1981.
[74] Sorenson H.W.. Kalman filtering Theory and Applications[M]. IEEE Press, 1985.
[75] 王晓华,敬忠良,陈非等.红外与激光主/被动联合跟踪算法[J].红外与激光工程.2000,30(4):298-300.
[76] 周荻.非线性滤波理论在制导和跟踪中的应用[D].哈尔滨工业大学博士论文,1996.
[77] 邓自立,王建国.带模型误差系统自适应Kalman滤波的虚拟噪声补偿技术[J].信息与控制.1988,17(5):1-5.
[78] Nahi, N.E. Optimal Recursive Estimation with Uncertain observation[J]. IEEE Transactions on Information Theory. 1969, 15(2): 475-480.
[79] Bar-shalom Y., Birmiwal K.. Variable dimension filter for maneuvering target tracking[J]. IEEE Transaction Aerospace Electronic Systems. 1982, 18(5): 611-619.
[80] Thorp J.S.. Optimal tracking of maneuvering targets[J]. IEEE Transactions on Aerospace Electronic System. 1973, 9(2): 512-519.
[81] Chan Y.T, Plant J.B., Botlomley J.R.T.. A Kalman tracking with a simple input estimator[J]. IEEE Transactions Electronic Systems. 1982, 18(2): 235-241.
[83] R.J. Fitzgerald. On reentry vehicle tracking in various coordinate system[J]. IEEE Transactions on Automatic Control. 1974, 19: 581-582.
[84] M.L.Andrade Netto.L.Gimeno, M.J.Mendes. On the optimal and suboptimal nonlinear filtering problem for discrete-time system[J]. IEEE Transactions on Automatic Control. 1978, 23: 1062-1067.
[85] 李树英.许茂增.随机系统的滤波与控制.北京:国防工业出版社,1991.
[86] 邓自立,王建国.非线性系统的自适应Kalman滤波[J].自动化学报.1987,13(5):375-379.
[87] 马佳光.预测目标角速度的最小平方滤波器[J].光学工程.1988,5:46-53.
[88] John Murray Fitts. Aided tracking as applied to high accuracy pointing system[J]. IEEE Transactions on Aerospace and Electronic Systems 1973, 9(3): 350-365.
[89] Robert A.Singer, Kenneth W. Behnke. Real-time tracking Filter evaluation and selecations for Tactical Application[J]. IEEE Transactions on Aerospace and Electronic Systems. 1971, 7(1): 100-106.
[90] 杨秀华,吉桐伯,陈涛,王延风.预测滤波技术在电视跟踪系统的应用[J].吉林大学学报(信息科学版),2003,21(4):247-250.
[91] YANG Xiu-hua, JI Tong-bo, CHEN Tao, Hart Xiao-quan, Wang Yan-feng, Zhao Yan. Application of prediction filtering technique in photoelectric tracking system[A]. 5th International Symposium on Test and Measurement(ISTM'2003), Vol.4: 2762-2764. (EI收录)
[92] 杨秀华,吉桐伯,陈涛,陈娟.测滤波技术在光电跟踪系统的应用[J].光与控制,2003,10(3):11-15.
[93] 杨秀华,吉桐伯,陈涛.卡尔曼滤波器在光电经纬仪的应用[J].测试技术学报,2003,17(4):324-328.(EI检索)
[94] 杨秀华,刘岩.系统分析工具GOMTOOL研究[J].光学精密工程.2000,8(3):278-282.
[95] 吉桐伯,陈娟,杨秀华,陈涛.地平式光电望远镜天顶盲区影响因素[J].光学精密工程.2003,11(3):296-300.
[96] JI Tong-bo, YANG Xiu-hua, CHEN Juan, CHEN Tao. Altitude-azimuth tracking performance and method around the zenith[A]. 5th International Symposium on Test and Measurement (ISTM'2003), Vol. 5: 3731-3734. (EI收录)
[97] 吉桐伯,杨秀华,陈涛,王建立,陈娟.地平式跟踪架近天顶跟踪性能影响因素分析(英文)[J].光电子激光.2004,(3).(EI检索)
[98] JI Tong-bo, YANG Xiu-hua, CHEN Juan, CHEN Tao. Analysis of tracking performance for altitude-azimuth pedestal near the zenith[A]. 5th International Symposium on Instrumentation and Control Technology (ISICT'2003), SPIE Vol.5253: 398-401. (EI检索)
[99] 范影乐,杨胜天,李轶等.MATLAB仿真应用详解[M].北京:人民邮电出版社,2001.
[100] 欧阳黎明.MATLAB控制系统设计[M].北京:国防工业出版社,2001.
[101] SEONG-Taek Park, JANG Gyu Lee. Design of a practical tracking algorithm with radar measurements[J]. IEEE Transactions on Aerospace and Electronic Systems. 1998, 34(4): 1337-1343.
[102] 王宏强,黎湘,刘丹.非线性系统中的机动目标跟踪算法[J].国防科技大学学报.2002,24(4):57-58.