弹道目标实时跟踪的稳健高精度融合滤波方法
|
摘要
弹道目标的高机动、大范围运动特征和复杂空天环境给测控系统带来了极大挑战,而新型测量设备的引入,在增大系统测量范围和提高局部精度的同时,也带来了测量信息不确定性的新变化。因此,为了获得弹道目标全程的良好跟踪,需要研究与之适应的稳健和高精度状态估计方法。
鉴于弹道目标运动轨迹的连续性和多测量设备的互补特性,本文采用实时滤波方法实现弹道运动先验信息、系统结构信息和设备测量信息的在线融合,用于确保弹道目标的稳健高精度跟踪。滤波方法研究中以弹道高精度运动建模和基于UKF的非线性滤波为主,同时引入系统误差自校准和粗差检择技术,提高系统跟踪精度和可靠性。在算法理论研究的基础上,还探讨了弹道目标跟踪数据融合处理系统的综合设计方法。论文主要研究成果如下:
(1)弹道目标运动的参数与半参数建模
在对比了常用弹道模型跟踪性能的基础上,确立了参数化和半参数化建模的研究思路。提出了基于带约束项自适应滑动多项式函数和基于自适应递推样条函数的参数化建模方法,它们均无需过多的目标受力和运动先验信息,形式灵活且具弹道表示精度高和机动检测能力强的优势。建立了弹道的递推半参数模型,并讨论了其建模参数的在线自适应选取方法,它的非参数分量可以对其它方法建模后的残余的高阶机动成分进行在线辨识,显著改善弹道估计精度。
(2)基于采样简化和双重滤波的简化UKF算法
建立了基于Kalman框架滤波算法的极大似然效能函数,用于算法推导与性能分析。根据系统不同程度的线性和可分性等先验信息,归纳和构建了4类简化采样的UKF算法,并以加性噪声系统为例,详细分析了状态扩展和重采样两个重要操作对UKF性能的影响,给出了实际系统中UKF的设计准则。基于分组滤波思想,采用随机性系统输入假设和顺序执行结构,提出了改进的DUKF算法,用于待估状态或参数可分的系统滤波。系统结构信息的利用,使得这些改进算法比原始UKF有更小的计算负担和更高的稳健性与估计精度。
(3)弹道目标跟踪中的系统误差自校准滤波技术
分析了系统误差对弹道估计结果的影响,确立了系统误差的分级校准方案。实现了开窗统计和EMBET两种参数化算法的递推估计形式,建立了复杂系统误差的递推半参数模型,基于稀疏表示理论,提出了系统误差所属通道的检测算法。这些算法充分利用了系统误差可模型化与所属通道稀疏化的特点,所需先验信息依次降低,可以在线同时实现系统误差的检测和自校准滤波。
(4)弹道目标跟踪中的粗差检择技术
分析了粗差在滤波器中的累积效应,并借鉴平差系统的可靠性理论,建立了Kalman滤波算法的可靠性评价方法。引入状态预测信息构建粗差检择标准和阈值,建立了面向状态的粗差检择算法,提高了成片粗差的检择能力。基于拟准观测思想,提出了拟准检定滤波算法,该算法可以显著提高滤波器抗粗差转移能力和粗差检择精度,尤其适用于观测结构复杂或观测数量较少的系统。改进滤波新息的加权方式,探讨了采用深度加权的稳健滤波算法,该算法利用同一时刻测元的相关性,在不直接进行粗差检择的情况下,有效实现了对粗差影响的抑制。
(5)弹道目标实时融合跟踪系统设计
建立了弹道目标跟踪任务的信息融合处理流程,详细设计了各处理环节的实现方案。采用模块化思想,综合设计了一个可以实现数据验证、试验模拟和实际应用的弹道目标跟踪仿真平台,该平台有较好的算法兼容性和任务扩展能力。
文中所用滤波方法和设计的融合系统在弹道目标跟踪领域取得了良好的处理效果。相关算法的实现策略和性能分析方法,以及一些主要结论均有一定的普适性,对一般性的动态系统状态估计问题和其它非线性滤波器设计问题同样有参考价值。
The trjactory target has a high maneuverability and wide range of dynamics as well as a complex measurement environment. This brings great challenges to the measurement and control systems. Meanwhile, the introductions of new instruments to the system not only mean an increasing coverage and a local improvable accuracy, but also bring new unceritainties of the measurement information. Therefore, robust and high accurate state estimation methods should been studied to obtain an excellent tracking performance throught the flight phases of the trajectory target.
Because of the continuity of trajectories and the complementary of measuring instruments, real-time fusion filters are used to ensure a robust and high accurate trajectory target tracking in this paper. Concretely, the prior dynamic charctersitic and the measuring information are integraed by high accurate dynamic modelings and nonlinear filters based on UKF. Meanwhile, the self-calibration of systematical error and detection of gross error are also regarded in detail. In addition, methods for actual fusion system designing are discussed in detail utilizing the data processing method here. The main contributes of this paper are listed as follows:
(1) Parmetric and semi-parmetric modeling for trajectory target
After the comparizations of the commonly used dynamic models for trajectory target, the researches are focused on the parametric and the semi-parametric modeling. For the parametric method, the adaptive sliding polynomial with equality constraint and the adaptive recursive spline are proposed. They do not need much prior information, such as the most substantial forces acting on the target or the dynamic charctersitic of the target. Therefore, the two methods are easy to implemented and have high accuracy and strongh ability of maneuver detection. For the semi-parametric method, a new recursive model with parameter selecting techniquies is developed for the real-time purpose. The new model can achieve an excellent improvement of tracking precision by introdcting non-parameter portions to identify the higher order maneuvers which cann’t be expressed by the commonly used model.
(2) Simplified UKF based on the strategies of simplified sampling and dual filter
First, the cost function basing on the maximum-likelihood principle is developed. Sencond, according to the linearity and separability of the system, four simplifications of UKF are proposed utilizing the different simplified sampling strategies. Then, taking the UKF in the additive noise case as an example, the influences of the state augmentation and sigma points resample are discussed in detail. On this basis, some universal designing principles for a practical UKF are given. Finally, an improved DUKF is derived to estimate the state and the parameter simultaneously. The new filter has a random control input and sequential dual estimation structure. The utilization of the prior structure information of the system make those improved algorithms have less calculation as well as better robustness and accuracy than the original UKF.
(3) Self-calibration of systematical error for trajectory target tracking A grading calibrated scheme is proposed, which is based on the impaction analysis
of systematical error on the filter results. First, for the two commonly used parametric method, i.e. the fitting of systematical error by using moving windows and the EMBET, forms of recursive estimation are realized. Secondly, a self-calibration filter based on semi-parameter modeling is investigated. Finally, the detection algorithms for the existence of systematical error are proposed by theory of sparse representation theory. Those algorothms require prior information decreasingly in turn. By taking full advantage of the modeling and sparseness of systematical error, they can realize the dectection and calibration of systematical error simultaneously online.
(4) Detection of gross error for trajectory target tracking
After the analysis of the cumulative effect on filter results for gross error, the evaluation for the reliability of Kalman filter is developed referring to the reliability theory in adjustment systems. Then, an algorithm so called detection facing to the state is introduced to construct the criterion and the threshold for gross error detection. Next, by integrating the selection of quasi-accurate observation and the Kalman framework, a new filter called quasi-accurate filter is developed. The algorithm has a strong resistance for the diversion of gross error and a notable detection accuracy, which makes it needs less quasi-accurate observations and more suitable for the systems with complexity observe structures or less observations. Finally, by improving the weighted mode of the innovation, the depth-weighted Kalman filter is proposed. The new filter utilizes the correlation of the observe information obtained at the same time and can release the effect of gross error without any straight detection steps.
(5) The design of the real-time fusion system for trajectory target tracking
First, the full data processing flow for trjectory target tracking system is suggested and the realization scheme of the system is dicussed in detail. Then, a simulation platform with modularized configuration is designed, which can be applied in data validaton, test simulation and practical application. The platform has a preferable compatibility for other algorithms and is easily extended to other tracking tasks.
The algorithms and the fusion systems in this paper have already been successfully used in the research on trajectory target tracking. Methods for the performance analysis and the filter designing here can also benefit researches on the state estimation of general dynamic systems and the design of other nonlinearal filters.
鉴于弹道目标运动轨迹的连续性和多测量设备的互补特性,本文采用实时滤波方法实现弹道运动先验信息、系统结构信息和设备测量信息的在线融合,用于确保弹道目标的稳健高精度跟踪。滤波方法研究中以弹道高精度运动建模和基于UKF的非线性滤波为主,同时引入系统误差自校准和粗差检择技术,提高系统跟踪精度和可靠性。在算法理论研究的基础上,还探讨了弹道目标跟踪数据融合处理系统的综合设计方法。论文主要研究成果如下:
(1)弹道目标运动的参数与半参数建模
在对比了常用弹道模型跟踪性能的基础上,确立了参数化和半参数化建模的研究思路。提出了基于带约束项自适应滑动多项式函数和基于自适应递推样条函数的参数化建模方法,它们均无需过多的目标受力和运动先验信息,形式灵活且具弹道表示精度高和机动检测能力强的优势。建立了弹道的递推半参数模型,并讨论了其建模参数的在线自适应选取方法,它的非参数分量可以对其它方法建模后的残余的高阶机动成分进行在线辨识,显著改善弹道估计精度。
(2)基于采样简化和双重滤波的简化UKF算法
建立了基于Kalman框架滤波算法的极大似然效能函数,用于算法推导与性能分析。根据系统不同程度的线性和可分性等先验信息,归纳和构建了4类简化采样的UKF算法,并以加性噪声系统为例,详细分析了状态扩展和重采样两个重要操作对UKF性能的影响,给出了实际系统中UKF的设计准则。基于分组滤波思想,采用随机性系统输入假设和顺序执行结构,提出了改进的DUKF算法,用于待估状态或参数可分的系统滤波。系统结构信息的利用,使得这些改进算法比原始UKF有更小的计算负担和更高的稳健性与估计精度。
(3)弹道目标跟踪中的系统误差自校准滤波技术
分析了系统误差对弹道估计结果的影响,确立了系统误差的分级校准方案。实现了开窗统计和EMBET两种参数化算法的递推估计形式,建立了复杂系统误差的递推半参数模型,基于稀疏表示理论,提出了系统误差所属通道的检测算法。这些算法充分利用了系统误差可模型化与所属通道稀疏化的特点,所需先验信息依次降低,可以在线同时实现系统误差的检测和自校准滤波。
(4)弹道目标跟踪中的粗差检择技术
分析了粗差在滤波器中的累积效应,并借鉴平差系统的可靠性理论,建立了Kalman滤波算法的可靠性评价方法。引入状态预测信息构建粗差检择标准和阈值,建立了面向状态的粗差检择算法,提高了成片粗差的检择能力。基于拟准观测思想,提出了拟准检定滤波算法,该算法可以显著提高滤波器抗粗差转移能力和粗差检择精度,尤其适用于观测结构复杂或观测数量较少的系统。改进滤波新息的加权方式,探讨了采用深度加权的稳健滤波算法,该算法利用同一时刻测元的相关性,在不直接进行粗差检择的情况下,有效实现了对粗差影响的抑制。
(5)弹道目标实时融合跟踪系统设计
建立了弹道目标跟踪任务的信息融合处理流程,详细设计了各处理环节的实现方案。采用模块化思想,综合设计了一个可以实现数据验证、试验模拟和实际应用的弹道目标跟踪仿真平台,该平台有较好的算法兼容性和任务扩展能力。
文中所用滤波方法和设计的融合系统在弹道目标跟踪领域取得了良好的处理效果。相关算法的实现策略和性能分析方法,以及一些主要结论均有一定的普适性,对一般性的动态系统状态估计问题和其它非线性滤波器设计问题同样有参考价值。
The trjactory target has a high maneuverability and wide range of dynamics as well as a complex measurement environment. This brings great challenges to the measurement and control systems. Meanwhile, the introductions of new instruments to the system not only mean an increasing coverage and a local improvable accuracy, but also bring new unceritainties of the measurement information. Therefore, robust and high accurate state estimation methods should been studied to obtain an excellent tracking performance throught the flight phases of the trajectory target.
Because of the continuity of trajectories and the complementary of measuring instruments, real-time fusion filters are used to ensure a robust and high accurate trajectory target tracking in this paper. Concretely, the prior dynamic charctersitic and the measuring information are integraed by high accurate dynamic modelings and nonlinear filters based on UKF. Meanwhile, the self-calibration of systematical error and detection of gross error are also regarded in detail. In addition, methods for actual fusion system designing are discussed in detail utilizing the data processing method here. The main contributes of this paper are listed as follows:
(1) Parmetric and semi-parmetric modeling for trajectory target
After the comparizations of the commonly used dynamic models for trajectory target, the researches are focused on the parametric and the semi-parametric modeling. For the parametric method, the adaptive sliding polynomial with equality constraint and the adaptive recursive spline are proposed. They do not need much prior information, such as the most substantial forces acting on the target or the dynamic charctersitic of the target. Therefore, the two methods are easy to implemented and have high accuracy and strongh ability of maneuver detection. For the semi-parametric method, a new recursive model with parameter selecting techniquies is developed for the real-time purpose. The new model can achieve an excellent improvement of tracking precision by introdcting non-parameter portions to identify the higher order maneuvers which cann’t be expressed by the commonly used model.
(2) Simplified UKF based on the strategies of simplified sampling and dual filter
First, the cost function basing on the maximum-likelihood principle is developed. Sencond, according to the linearity and separability of the system, four simplifications of UKF are proposed utilizing the different simplified sampling strategies. Then, taking the UKF in the additive noise case as an example, the influences of the state augmentation and sigma points resample are discussed in detail. On this basis, some universal designing principles for a practical UKF are given. Finally, an improved DUKF is derived to estimate the state and the parameter simultaneously. The new filter has a random control input and sequential dual estimation structure. The utilization of the prior structure information of the system make those improved algorithms have less calculation as well as better robustness and accuracy than the original UKF.
(3) Self-calibration of systematical error for trajectory target tracking A grading calibrated scheme is proposed, which is based on the impaction analysis
of systematical error on the filter results. First, for the two commonly used parametric method, i.e. the fitting of systematical error by using moving windows and the EMBET, forms of recursive estimation are realized. Secondly, a self-calibration filter based on semi-parameter modeling is investigated. Finally, the detection algorithms for the existence of systematical error are proposed by theory of sparse representation theory. Those algorothms require prior information decreasingly in turn. By taking full advantage of the modeling and sparseness of systematical error, they can realize the dectection and calibration of systematical error simultaneously online.
(4) Detection of gross error for trajectory target tracking
After the analysis of the cumulative effect on filter results for gross error, the evaluation for the reliability of Kalman filter is developed referring to the reliability theory in adjustment systems. Then, an algorithm so called detection facing to the state is introduced to construct the criterion and the threshold for gross error detection. Next, by integrating the selection of quasi-accurate observation and the Kalman framework, a new filter called quasi-accurate filter is developed. The algorithm has a strong resistance for the diversion of gross error and a notable detection accuracy, which makes it needs less quasi-accurate observations and more suitable for the systems with complexity observe structures or less observations. Finally, by improving the weighted mode of the innovation, the depth-weighted Kalman filter is proposed. The new filter utilizes the correlation of the observe information obtained at the same time and can release the effect of gross error without any straight detection steps.
(5) The design of the real-time fusion system for trajectory target tracking
First, the full data processing flow for trjectory target tracking system is suggested and the realization scheme of the system is dicussed in detail. Then, a simulation platform with modularized configuration is designed, which can be applied in data validaton, test simulation and practical application. The platform has a preferable compatibility for other algorithms and is easily extended to other tracking tasks.
The algorithms and the fusion systems in this paper have already been successfully used in the research on trajectory target tracking. Methods for the performance analysis and the filter designing here can also benefit researches on the state estimation of general dynamic systems and the design of other nonlinearal filters.
引文
[1]朱炬波.不完全弹道测量的数据融合技术[J].科学通报, 2000, 45(20): 2236~2240.
[2]郝岩.航天测控网[M].北京:国防工业出版社, 2004.
[3]王德纯,丁家会,程望东等.精密跟踪测量雷达技术[M].电子工业出版社, 2006.
[4]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社, 2006.
[5]余安喜.多传感器量测融合技术研究[D],长沙:国防科学技术大学, 2003.
[6] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part VI: approximation techniques for nonlinear filtering[C]. Proceedings of 2004 SPIE Conference on Signal and Data Processing of Small Targets, San Diego, CA, USA, April 2004: 537~550.
[7] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part II: ballistic target models[C]. Proceedings of 2001 SPIE Conference. on Signal and Data Processing of Small Targets, San Diego, CA, USA, 2001(4473-63): 1~23.
[8] Li X R and Jilkov V P. A survey of maneuvering target tracking Part I: dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333~1364.
[9]王正明,易东云.测量数据建模与参数估计[M].长沙:国防科技大学出版社, 1996.
[10] Wang Z M, Zhu J B. Reduced parameter model on trajectorytracking data with applications[J]. Science in China (Series E), 1999, 42(2): 190~199.
[11] Green P J, Silverman B W. Nonparametric regression and generalized linear models. London: Chapman and Hall, 1994: 17~2l.
[12]丁士俊.测量数据的建模与半参数估计[D].武汉:武汉大学, 2005.
[13] Ruppert D, Wand M P and Carroll R J. Semiparametric regression[M]. Cambridge : Cambridge university press, 2003.
[14]文援兰.航天器轨道确定理论[M].长沙:国防科学技术大学课程讲义, 2005.
[15] Blair J. Error estimates derived from the data for least-squares spline fitting [C]. Instrumentation and Measurement Technology Conference - IMTC 2007 Warsaw, Poland, May 1-3, 2007: 1~6.
[16] Liang Y Q and Han C Z. Tracking of semi-ballistic reentry vehicle[C]. The 2008 IEEE Multi-conference on Systems and Control, San Antonio, Texas, USA, 2008: 3~5.
[17]易东云,朱炬波,王正明等.多站连续波跟踪雷达的测速定轨研究[J].中国空间科学技术, 1998(5): 19~24.
[18]朱炬波,王正明,易东云等.测速定轨的实时算法[J].宇航学报,2001,22(6): 119~123.
[19] He, X, L. Shen, and Shen Z., A data-adaptive knot selection scheme for fitting splines[J]. IEEE Signal Processing Letters, 2001, 8(5): 137~139.
[20]邱恺,黄国荣,陈天如等.卡尔曼滤波过程的稳定性研究[J].系统工程与电子技术, 2005, 27(1): 33~35.
[21] Yiu, K F C, Wang S and Kok L T et al. Nonlinear system modeling via knot-optimizing B-spline networks[J]. IEEE Transactions on Neural Networks, 2001, 12(5): 1013~1022.
[22] Blair, J., Optimal knot selection for least-squares fitting of noisy data with spline functions[C]. IMTC 2008 IEEE International Instrumentation and Measurement Technology Conference Victoria, Vancouver Island, Canada, May 12~15, 2008: 1~6.
[23] Liu J Y, Zhu J B and Xie M H. Trajectory estimation based on the sparse representation and spline model optimization in multi-range-rate system[J]. Chinese Journal of Aeronautics. 2010, 23(1): 84~90.
[24]周海银.基于测速定轨的一类自适应样条滤波方法[J].国防科技大学学报, 2001, 23(6): 38~41.
[25]段晓君,周海银.基于扩展Kalman滤波的实时测速定轨算法[J].中国空间科学技术, 2001(6): 20~25.
[26] Basin M and Calderon D. Optimal filtering for incompletely measured polynomial states with multiplicative noise[C]. 2008 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA, 2008: 4244~4249.
[27]高羽,张建秋,尹建君.机动目标的多项式预测模型及其跟踪算法[J].航空学报, 2009(8): 1479~1489.
[28] Cai R T, Wu Q X and Liu J Q. Memoryless Polynomial RLS Adaptive filter for trajectory target tracking[C]. Second International Conference on Computer Modeling and Simulation, 2010: 235~238.
[29]董劲男,秦贵和,张晋东等.基于多项式预测滤波理论的实时信号传输[J].吉林大学学报(工学版), 2008, 38(4): 890~896.
[30] Chanier F. Paul C and Blanc C ea tl. SLAM Process using polynomial extended Kalman filter: experimental assessment[C]. 10th Intl. Conf. on Control, Automation, Robotics and Vision Hanoi, Vietnam, 2008: 365~370.
[31] Germani A, Manes C. The Polynomial Extended Kalman Filter as an exponential observer for nonlinear discrete-time systems[C]. Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, 2008: 5122~5127.
[32] Farina A, Immediata S and Timmoneri L et al. Comparison of recursive and batch processing for impact point prediction of ballistic targets[C]. IEEE Int. Radar Conf., 2005: 121~126.
[33] Bailey T, Nieto J and Guivant J et al. Consistency of the EKF-SLAM algorithm[C]. IEEE International Conference on Robots and Systems, Beijing, China, 2006: 3562~3568.
[34] Crouse D F, Willett P and Bar-Shalom Y. A low-complexity sliding-window Kalman FIR smoother for discrete-time models[J]. IEEE Signal Processing Letters, 2010, 17(2): 177~180.
[35]张贤达.矩阵分析与应用[M].北京:清华大学出版社, 2004.
[36] Villien C, Ostertag P. A polynomial approximation algorithm for real-time maximum-likelihood estimation[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2082~2095.
[37]刘吉英.基于特征信息建模的高精度目标定位[D].长沙:国防科学技术大学, 2006.
[38] Ramsay J O , Silverman B W. Functional data analysis(Second Edition)[M]. Springer Science, 2005.
[39] Zhang D W and Lin X H. Variance component testing in generalized linear mixed models for longitudinal/clustered data and other related topics[J]. Lecture Notes in Statistics, 2008, 192, Part I: 19~36.
[40] Fan J Q, Lin X H and Lu J S. New developments in biostatistics and bioinformatics[M]. Beijing: Highe Education Press, 2009.
[41] Lu W, Andera R and Lin X H. Nonparametric regression with missing outcomes using weighted kernel estimating equations[J]. Journal of the Ameerican Statistical Associaltion, 2010, 105(491): 1135~1146.
[42] Zhang S Y, Lin X H. Nonparmetric regession using local kernel estimation equations for correlated filure time data[J]. Oxford Journals Mathematics&Physical Sciences Biometrika. 2008, 95(1): 123~137.
[43] Lin X H and Zhang D W. Inference ingenralized additive mixed models by using smoothing splines[J]. J R Statist Soc B, 1999, 61: 381~400.
[44] Li Y S, Lin X H and Peter M. Bayesian inference in semoiparametric mixed models for longitudinal data[J]. Biometerics Journal of the International Biometric Society, 2010, 66(1): 70~78.
[45] Engle R F, Granger C W J and Rice J et al. Semiparametric estimates of the relation between weather and electricity sales[J]. JASA, 1986(81): 310~320.
[46]邱卫宁,陶本藻,姚宜斌等.测量数据处理理论与方法[M].武汉:武汉大学出版社, 2008.
[47]潘晓刚,李济生,段晓君等.天基空间目标监视与跟踪系统轨道确定技术研究[J].自然科学进展, 2008, 18(11): 1226~1239.
[48]潘旺华,文援兰,廖瑛.基于半参数回归模型的批处理确定卫星轨道方法[J].宇航学报, 2008. 29(6): 1917~1921, 1954.
[49]潘雄,半参数模型的估计理论及其应用[D].武汉:武汉大学, 2005.
[50]周宏仁等.机动目标跟踪[M].北京:国防工业出版社, 1991.
[51] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part IV: decision-based methods[C]. Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, Orlando, FL, USA, April 2002(4728-60): 1~24.
[52] Ru J, Jilkov V P, and Li X R et al. Sequential detection of target maneuvers[C]. 7th International Conference on Information Fusion (FUSION), 2005: 345~351.
[53] Nikiforov I V. A lower bound for the detection/isolation delay in a class of sequential tests[J]. IEEE Transactions on Information Therory, 2003, 49(11): 3037~3047.
[54] Veeravalli V V. Decentralized quickest change detection[C]. IEEE IEEE Transactions on Information Therory, 2001, 47(4): 1657~1665.
[55] Shi J F, Q L and He C W. A maneuvering target detecting method based on lifting wavelet transform algorithm[C]. CIS2008, 2008: 710~713.
[56] Ji H B, Song L P and Gao X b. Higher-order cumulants based maneuver detection in Kalman Filter[C]. Proceedings of IEEE TENCON’02, 2002: 1385~1388.
[57] Duh F B and Lin C T. Tracking a maneuvering target using neural fuzzy network[J]. IEEE Transanctions on Systmens, Man and Cybernietics—Part B: Cybernatics, 2004, 34(1): 16~33.
[58] Bahari M H, Karsaz A and Khaloozadeh H. New algorithm based on combined fuzzy logic and Kalman filter for target maneuver detection[C]. The 1st International Symposium on systems and control in aerospace and astronautics, 2006: 520~524.
[59]杨元喜,文援兰.卫星精密轨道综合自适应抗差滤波技术[J].中国科学D辑, 2003, 3(11): 1112~1119.
[60] Zheng D W, Zhong P and Ding X L et al. Filtering GPS time-series using a Vondrak filter and gross-validation[J]. Journal of Geodesy, 2005, 79(6): 363~369.
[61]谷德峰等.抗差Vondrak滤波器设计及其在粗差探测中的应用[J].兵工学报, 2008, 29(6):696~700.
[62] Blom H A P and Bar-shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Transactionson Automatic Control, 1988, AC-33(8): 780~783.
[63] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part V:multiple-moel methods[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003(11): 1~57.
[64] Liu J S and Lin R H. A Hierarchical adaptive interacting multiple model algorithm[C]. 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007: 699~703.
[65]段琢华,蔡自兴,丁金霞.不完备多模型混合系统故障诊断的粒子滤波算法[J].自动化学报, 2008, 34(5): 581~587.
[66] Hamed S G and Mohammad B M. A Systematic fuzzy decision-making process to choose the best model among a set of competing models[J]. IEEE Transanctions on Systmens, Man and Cybernietics—Part A: Systems and Humans, 2008, 38(5): 1118~1128.
[67]潘泉,杨峰,叶亮等.一类非线性滤波器——UKF综述[J].控制理论与决策, 2005, 20(5): 481~489.
[68] Julier S J and Uhlmann J K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations[C]. Proceedings of the American Control Conference, Jefferson City, 2002: 887~892.
[69] Farina A and Ristic B. Tracking a ballistic target: comparison of several nonlinear filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(3): 854~867.
[70]柴霖,袁建平.非线性估计理论的最新进展[J].宇航学报, 2005, 26(3): 380~384.
[71] Schei T S. A finite difference method for linearization in nonlinear estaimation algorithms[J]. Automatica, 1997, 33(11): 2051~2058.
[72] N?rgaard M, Poulsen N K and Ravn O. New developments in state estimation for nonlinear system [J]. Automatica, 2000, 36(11): 1627~1638.
[73] Simon Maskell, Neil Gordon. A tutorial on particle filters for on-line nonlinear/non-Guasian bayesian tracking[J]. IEEE Transaction on Signal Processing, 2001: 1~14.
[74]张红梅,邓正隆,林玉荣.一种基于模型误差的UKF方法[J].航空学报, 2004, 25(6): 598~601.
[75]何秀凤,杨光.扩展区间Kalman滤波器及其在GPS/INS组合导航中的应用[J].测绘学报, 2004, 33(1): 47~52.
[76]杨小军.粒子滤波进展与展望[J].控制与决策, 2006, 23(2): 262~267.
[77] Rudolph der Merwe Doucet A, and de Freitas N et al.The unscented particle filter [C]. Advances in Neural Information Proc Systems (NIPS13), 2004: 1~7.
[78] Li S J, Wang H and Chai TY. A t-distribution based particle filter for target tracking[C].Proceedings of the 2006 American Control Conference, Minneapolis,Minnesota, USA, June 14-16, 2006: 2191~2196.
[79] Oswald L. Approximate bayesian multi-body tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1436~1449.
[80] Julier S J and Uhlmann J K. A new extension of the Kalman filter to nonlinear systems[C]. The Proceedings of AeroSense: 11th International Symposium Aerospace/Defense Sensing, Simulation and Controls, Orlando, 1997: 54~65.
[81] Harini. Deterministic sampling-based switching Kalman filtering for vehicle tracking[C]. IEEE Intelligent Transportation Systems Conference 2006, Toronto, Canada, September, 2006: 17~20.
[82] Ge X G, Jiang J and Ma X Y. Multiple moving platforms and multi-sensor maneuvering target tracking based on fuzzy adaptive UKF[C]. IMACS Multiconference on Computational Engineering in Systems Applications(CESA), Beijing, China, 2006: 2015~2020.
[83] Zhan R H and Wan J W. Neural network-aided adaptive unscented Kalman Filter for nonlinear state estimation[J]. IEEE Signal Processing Letters, 2006, 13(7): 445~448.
[84] Ananthasayanam M R, Sarkar A K and Bhattacharya A et al. Nonlinear observer state estimation from seeker measurements and seeker-radar measurements fusion[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit 15-18 August 2005, San Francisco, California, 2005: 1~25.
[85] Daum F. Nonlinear filters: beyond the Kalman filter[J]. IEEE Aerospace and Electronics System Magazine, 2005, 20(8): 57~69.
[86] Wu Y X, Hu D W and Wu M P et al. Unscented Kalman filtering for additive noise case: augmented versus nonaugmented[J]. IEEE Signal Processing Letters, 2005, 12(5): 357~360.
[87] Wang E A and Merwe R. The unscented Kalman filter, in Kalman filtering and neural networks[M]. S. Haykin, Ed. N York: Wiley Pub, 2001: 221~280.
[88] Hao Y, Xiong Z and Sun F et al. Comparison of unscented Kalman filters[C]. Proceedings of the 2007 IEEE Intern Conference on Mechanic and Automatic, Harbin, China, 2007: 895~899.
[89] Li H, Xu D, Jun J et al. Sequence Unscented Kalman filtering algorithm[C]. The 3rd IEEE Conference on Industry and Electronic Application, 2008 (ICIEA 2008): 1374~1378.
[90] Xu J, Jing Y and Georgi M et al. Two-stage unscented Kalman filter for nonlinear systems in the Presence of unknown random bias[C]. 2008 American Control Conference, Seattle, Washington, USA, 2008: 3530~3535.
[91]钟慧敏,房建成.基于非线性模型预测滤波的微纳卫星定位方法[J].北京航空航天大学学报, 2008, 34(11): 1339~1342.
[92] van der Merwe R. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models[D]. Oregen: Oregon Health & Science University, 2004.
[93] Mookerjee P and Reifler F. Reduced state estimator for systems with aparametric inputs[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(2): 446~461.
[94] Haessig D and Friedland B. Separate-bias estimation with reduced-order Kalman filters[J]. IEEE Transactions on Automatic, 1998, 43(7): 983~987.
[95] Mariani S and Corigliano A. Impact induced composite delamination: state and parameter identification via joint and dual extended Kalman filters[J]. Computer Methods in Applied mechanics and engineering, 2005, 194: 5242~5272.
[96] Moradkhani H, Sorooshian S and Gupta H V et al. Dual state–parameter estimation of hydrological models using ensemble Kalman filter[J]. Advances in Water Resources, 2005, 28: 135~147.
[97] Kim, K H, Lee J G and Park C G. Adaptive two-stage extended Kalman filter for a fault-tolerant INS-GPS loosely coupled system[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(1): 125~137.
[98] Lange K A. Gradient algorithm locally equivalent to the EM algorithm[J]. Journal of the Royal Statistical Society, Series B, 1995, 59(2): 425~437.
[99] Golub G and Pereyra V. Separable nonlinear least squares: the variable projection method and its applications[J]. Inverse Problems, 2003, 19: 1~26.
[100] Wan E A, van der Merwe R and Nelson A T. Dual estimation and the unscented transformation, in neural information processing systems[M]. Massachusetts: MIT press, 2000: 666~672.
[101] Ruhe A and Wedin P A. Algorithms for separable nonlinear least squares problems[J]. SIAM Review, 1980, 22: 318~337.
[102] Campillo F and Rossi V. Convolution particle filter for parameter estimation in general state-space models[J]. IEEE Transactions on Aerospace and Electronic Systems, 45, 2009, 3: 1063~1072.
[103] Wan E A and Nelson A T. Neural dual extended Kalman filtering: applications in speech enhancement and monuaral blind signal separation[C]. In IEEE Workshop on Neural Networks for Signal Processing, 1997: 466~475.
[104] Gannot S and Moonen M. On the application of the unscented Kalman filter to speech processing[C]. Proc. Int. Workshop Acoust. Echo Noise Control (IWAENC), 2003: 1~4.
[105] Tian X J, Xie Z H and Dai A G. A land surface soil moisture data assimilation system based on the dual-UKF method and the community land model[J]. Journal of Geophysical Research, 2008, 113(D14127): 1~11.
[106] Jamoos A, Grivel E and Shakarneh N et al. Dual H∞vs dual Kalman filters forM-AR parameter estimation form noisy observations[C]. 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland. 2009: 1700~1704.
[107] He F and Wu L. Sequential dual filter based smoothing framework for integrated navigation systems[C]. Instrumentation and Measurement Technology Conference Proceedings (2007IMTC), Warsaw, Poland, 2007: 1~5.
[108] Gove J H and Hollinger D Y. Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface-atmosphere exchange[J]. Journal of Geophysical Research, 2006, 111(D08S07): 1~21.
[109] Hegyi A, Girimonte D and Babuska et al. A comparison of filter configurations for freeway traffic state estimation[C]. 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada. 2006: 1029~1034.
[110] Zhang H and Shahriar N. EKF-based recursive dual estimation of structure and motion from stereo data[J]. IEEE Journal of Oceanic Engineering, 2010, 35(2): 424~437.
[111] Wan E A and Nelson A. T Dual Kalman filtering methods for nonlinear prediction, estimation and smoothing[C]. In Advances in Neural Information Processing Systems, 1997, 9: 1~7.
[112] Nelson A T and Wan E A. A two-observation Kalman framework for maximum-likelihood modeling of noisy time series[C]. The 1998 IEEE International Joint Conference on Neural Networks Proceedings, IEEE World Congress on Computational Intelligence, 1998: 2489~2494.
[113] Jiang Z, Song Q and He Y Q et al. A novel adaptive unscented Kalman filter for nonlinear estimation[C]. Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, 2007: 4293~4298.
[114] Soken H E and Hajiyev C. Pico satellite attitude estimation via robust unscented Kalman filter in the presence of measurement faults[J]. ISA Transactions, 2010: 249~256.
[115] Durovic Z M and Kovacevic B D. Robust estimation with unknown noise statistics[J]. IEEE Transactions on Automatic Control, 1999, 44(6): 1292~1296.
[116]杨元喜,张双成.导航解算中的系统误差及其协方差矩阵拟合[J].测绘学报, 2004, 33(3): 190~194.
[117]易东云,朱炬波,王正明等.多站连续波跟踪雷达的测速定轨研究[J].中国空间科学技术, 1998, 18(5): 19~24.
[118] Emmanuel J C, Michael B W. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008: 21~30.
[119] Vaswani N. Kalman filtered compressed sensing [C]. In IEEE International Conference on Image Proc(ICIP), 2008: 893~896.
[120] Vaswani N. Analyzing least squares and Kalman filtered compressed sensing[C].ICASSP 2009, 2009: 3013~3016.
[121] Qiu C, Lu W and Vaswani N. Real-time dynamic MR image reconstruction using Kalman filtered compressed sensing[C]. In ICASSP 2009, 2009: 393~396.
[122]胡绍林,孙国基.靶场外测数据野值点的统计诊断技术[J].宇航学报, 1999(2): 68~74.
[123]肖艳军,李建勋.抗野值多速率模型及交互式多传感器状态融合[J].自然科学进展, 2005, 15(9): 1106~1112.
[124]张磊,汪渤.抗野值H∞滤波在组合导航中的应用[J].北京理工大学学报, 2009, 29(7): 600~604.
[125]杨元喜. Kalman滤波异常误差检测[J].测绘科学与工程, 2005, 25(4): 1~4.
[126]荣思远,穆荣军,崔乃刚. EKF容错滤波方法在磁测自主导航中的应用研究[J].电子学报, 2006, 34(12): 2268~2271.
[127]陈轲,归庆明,柳丽等. Gauss-Markov模型的t型抗差估计[J].测绘学报, 2008, 37(3): 281~284, 292.
[128] Huber P J. Robust statistics[M]. New York: John Wiley&Sons, 1981.
[129] Ting J, Theodorou E and Schaal S. A Kalman filter for robust outlier detection[C]. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems San Diego, CA, USA, 2007: 1514~1519.
[130] Ou J K. A new method of identifying and locating gross errors—quasi-accurate detection[J]. Chinese Sciences Bulletin, 1999, 44(10): 1777~1781.
[131]柴艳菊,欧吉坤. Kalman滤波质量控制的一种改进算法[J].自然科学进展, 2004, 14(8): 904~909.
[132] Martin R D, Yohai V J. Influence functional for time series[J]. Ann Statist, 1986, 14(3): 781~819.
[133] Barner K E and Aysal T C. Polynomial weighted median filtering[J]. IEEE Transactions on Signal Processing, 2006, 54(2): 636~650.
[134] Lee H and Roberts S J. On-line novelty detection using the Kalman filter and extreme value theory[C]. 19th International Conference on Pattern Recognition, 2008: 1~4.
[135] Atanassov R, Bose P and Couture M et al. Algorithms for optimal outlier removal[J]. Journal of Discrete Algorithms, 2009(7): 239~248.
[136] Maiz C S, Miguez J and Djuric P M. Particle filtering in the presence of outliers[C]. IEEE/SP 15th Workshop on Statistical Signal Processing, 2009: 33~36.
[137] Zuo Y J, Cui H J. Statistical depth functions and some applications[J]. Advances in mathematics, 2004, 33(1): 1~25.
[138] Sara L P, Juan R. Depth-based inference for functional data[J]. ComputationalStatistics & Data Analysis, 2007, 51: 4957~4968.
[139]潘晓刚,周海银.卫星轨道确定模型误差的深度加权核估计方法[J].天文学报, 2009, 50(1): 85~97.
[140]李强,吴翊.ρ混合样本线性模型M估计的强相合性[J].应用数学, 2007, 20(3): 381~385.
[141]欧吉坤.测量数据的质量控制理论探讨[J].测绘工程, 2001, 10(2): 6~10.
[142]朱炬波.不完全测量数据的建模、处理及应用[D].长沙:国防科技大学, 2004.
[143]吴旭光.水下自主航行器动力学模型——建模和参数估计[M].西安:西北工业大学出版社, 1998.
[144] Svehla D and Rothacher M. Kinematic and reduced-dynamic precise orbit determination of low earth orbiters[J]. Advances in Geosciences, 2003, 1: 47~56.
[145] Dankovic B, Antic D, Jovanovic Z et al. Systems modeling based on Legendre polynomials[C]. The 5th International Symposium on Applied Computational Intelligence and Informatics, Timi?oara, Romania, 2009: 241~146.
[146] Chen Z. Local observability and its application to multiple measurement estimation[J]. IEEE Transactions on Industrial Electronics, 1991, 38(6): 491~496.
[147]周卫东,乔相伟,吉宇人等.基于新息和残差的自适应UKF算法[J].宇航学报, 2010, 31(7): 1798~1804.
[148] Li X R. Kalman filter versus IMM estimator: when do we need the latter? [J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1452~1457.
[149]范小军,刘锋.一种新的机动目标跟踪的多模型算法[J].电子与信息学报, 2007, 29(3): 532~535.
[150] Julier S J and Laviola J J. On Kalman filtering with nonlinear equality constraints[J]. IEEE Transations on Signal Processing, 2007, 55(6): 2774~2784.
[151]孙海燕,潘雄.正规化矩阵正定时半参数估计量的统计性质[J].测绘学报, 2004, 33(3): 228~232.
[152] Briers M, Maskell S R and Wright R. A rao-blackwellised unscented Kalman filter[C]. Proceedings of the Sixth Int Conference of Information Fusion, Cairns Australia 2003: 1~7.
[153] Sch?n T, Gustafsson F and Nordlund P. Marginalized particle filters for mixed linear/nonlinear state-space models[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2279~2289.
[154] Kol S, Fossa B A and Scheic T S. Constrained nonlinear state estimation based on the UKF approach[J]. Computers and Chemical Engineering, 2009(33):1386~1401.
[155] Tian X and Bar-Shalom Y. Coordinate conversion and tracking for very long range radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 1073~1088.
[156] Xu J, Georgi M D and Yuan W J et al. UKF design and stability for nonlinear stochastic systems with correlated noises[C]. Proceedings of the 46th IEEE Conference of Decision and Control, New Orleans, LA, USA, 2007: 6226~6231.
[157] Hegyi A, Girimonte D and Babuska R et al. A comparison of filter configurations for freeway traffic state estimation[C]. 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada, 2006: 1029~1034.
[158]李强.单星对卫星目标的被动定轨与跟踪关键技术研究[D].长沙:国防科学技术大学, 2007.
[159] Mackey M and Glass L. Oscillations and chaos in physiological control systems[J]. Science, 1997, 4300: 287~289.
[160] van der Merwe R. The ReBEL toolkit website. Available: http://choosh.bme.ogi.edu/rebel.
[161]赵汉元编著.飞行器再入动力学和制导[M].长沙:国防科技大学出版社, 1997.
[162]周宏潮,王正明.线性模型信号估计的精度分析[J].宇航学报, 2005, 26(增): 145~148.
[163]陶本藻,施闯,姚宜斌.顾及系统误差的平差模型研究[J].测绘学院学报, 2002, 19(2): 79~81.
[164]曹轶之,隋立芬,范澎湃.一种Kalman滤波系统误差及其协方差矩阵的半参数估计方法[J].测绘科学, 2009, 34(2): 64~66,84.
[165]赵德勇,王正明,潘晓刚等.基于半参数回归的联合定轨误差估计仿真算法[J].系统仿真学报, 2007, 19(8): 1692~1695.
[166] Candes E J, Romberg J and Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transations on Information Theory, 2006, 52(2): 489~509.
[167] Carmi A, Gurfil P and Kanevsky D. Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms[J]. IEEE Transactions on Signal Processing, 2010, 58(4): 2405~2409.
[168] Shamaiah M and Vikalo H. Rao-Blackwellized unsented Kalman filter for nonlinear systems with bandwidth constraints[C]. ICASSP 2010, 2010: 2642~2645.
[169] James G M, Radchenko P and Lv J. DASSO: Connections between the dantzig selector and LASSO[J]. Journel of the Royal Satatistical Society Series B(Methodological), 2007, 55(6): 2774~2784.
[170]周卫东,乔相伟,吉宇人等.基于新息和残差的自适应UKF算法[J].宇航学报, 2010, 31(7): 1798~1804.
[171] Morales Y, Takeuchi E and Tsubouchi T. Vehicle localization in outdoor woodland environments with sensor fault detection[C]. 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 2008: 449~454.
[172] Cetin M. Robust model selection criteria for robust Liu estimator[J]. European Journal of Operational Research, 2009: 21~24.
[173] Zuo Y J, Cui H J, He X M. On the stahel-Donoho estimator and depth-weighted means of multivariate data[J]. Ann Statist, 2004, 32(1): 483~499.
[174] Bar-Shalom Y. Update with out-of-sequence measurements in tracking: exact solution[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(3): 769~777.
[175] Julier S J. The spherical simplex unscented transformation[C]. Proceedings of the American Control Conference, Denver, 2003: 2430~2434.
[176] Julier S J. The scaled unscented transformation[C]. Proceedings of the American Control Conference, Jefferson City, 2002: 4555~4559.
[2]郝岩.航天测控网[M].北京:国防工业出版社, 2004.
[3]王德纯,丁家会,程望东等.精密跟踪测量雷达技术[M].电子工业出版社, 2006.
[4]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社, 2006.
[5]余安喜.多传感器量测融合技术研究[D],长沙:国防科学技术大学, 2003.
[6] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part VI: approximation techniques for nonlinear filtering[C]. Proceedings of 2004 SPIE Conference on Signal and Data Processing of Small Targets, San Diego, CA, USA, April 2004: 537~550.
[7] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part II: ballistic target models[C]. Proceedings of 2001 SPIE Conference. on Signal and Data Processing of Small Targets, San Diego, CA, USA, 2001(4473-63): 1~23.
[8] Li X R and Jilkov V P. A survey of maneuvering target tracking Part I: dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333~1364.
[9]王正明,易东云.测量数据建模与参数估计[M].长沙:国防科技大学出版社, 1996.
[10] Wang Z M, Zhu J B. Reduced parameter model on trajectorytracking data with applications[J]. Science in China (Series E), 1999, 42(2): 190~199.
[11] Green P J, Silverman B W. Nonparametric regression and generalized linear models. London: Chapman and Hall, 1994: 17~2l.
[12]丁士俊.测量数据的建模与半参数估计[D].武汉:武汉大学, 2005.
[13] Ruppert D, Wand M P and Carroll R J. Semiparametric regression[M]. Cambridge : Cambridge university press, 2003.
[14]文援兰.航天器轨道确定理论[M].长沙:国防科学技术大学课程讲义, 2005.
[15] Blair J. Error estimates derived from the data for least-squares spline fitting [C]. Instrumentation and Measurement Technology Conference - IMTC 2007 Warsaw, Poland, May 1-3, 2007: 1~6.
[16] Liang Y Q and Han C Z. Tracking of semi-ballistic reentry vehicle[C]. The 2008 IEEE Multi-conference on Systems and Control, San Antonio, Texas, USA, 2008: 3~5.
[17]易东云,朱炬波,王正明等.多站连续波跟踪雷达的测速定轨研究[J].中国空间科学技术, 1998(5): 19~24.
[18]朱炬波,王正明,易东云等.测速定轨的实时算法[J].宇航学报,2001,22(6): 119~123.
[19] He, X, L. Shen, and Shen Z., A data-adaptive knot selection scheme for fitting splines[J]. IEEE Signal Processing Letters, 2001, 8(5): 137~139.
[20]邱恺,黄国荣,陈天如等.卡尔曼滤波过程的稳定性研究[J].系统工程与电子技术, 2005, 27(1): 33~35.
[21] Yiu, K F C, Wang S and Kok L T et al. Nonlinear system modeling via knot-optimizing B-spline networks[J]. IEEE Transactions on Neural Networks, 2001, 12(5): 1013~1022.
[22] Blair, J., Optimal knot selection for least-squares fitting of noisy data with spline functions[C]. IMTC 2008 IEEE International Instrumentation and Measurement Technology Conference Victoria, Vancouver Island, Canada, May 12~15, 2008: 1~6.
[23] Liu J Y, Zhu J B and Xie M H. Trajectory estimation based on the sparse representation and spline model optimization in multi-range-rate system[J]. Chinese Journal of Aeronautics. 2010, 23(1): 84~90.
[24]周海银.基于测速定轨的一类自适应样条滤波方法[J].国防科技大学学报, 2001, 23(6): 38~41.
[25]段晓君,周海银.基于扩展Kalman滤波的实时测速定轨算法[J].中国空间科学技术, 2001(6): 20~25.
[26] Basin M and Calderon D. Optimal filtering for incompletely measured polynomial states with multiplicative noise[C]. 2008 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA, 2008: 4244~4249.
[27]高羽,张建秋,尹建君.机动目标的多项式预测模型及其跟踪算法[J].航空学报, 2009(8): 1479~1489.
[28] Cai R T, Wu Q X and Liu J Q. Memoryless Polynomial RLS Adaptive filter for trajectory target tracking[C]. Second International Conference on Computer Modeling and Simulation, 2010: 235~238.
[29]董劲男,秦贵和,张晋东等.基于多项式预测滤波理论的实时信号传输[J].吉林大学学报(工学版), 2008, 38(4): 890~896.
[30] Chanier F. Paul C and Blanc C ea tl. SLAM Process using polynomial extended Kalman filter: experimental assessment[C]. 10th Intl. Conf. on Control, Automation, Robotics and Vision Hanoi, Vietnam, 2008: 365~370.
[31] Germani A, Manes C. The Polynomial Extended Kalman Filter as an exponential observer for nonlinear discrete-time systems[C]. Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, 2008: 5122~5127.
[32] Farina A, Immediata S and Timmoneri L et al. Comparison of recursive and batch processing for impact point prediction of ballistic targets[C]. IEEE Int. Radar Conf., 2005: 121~126.
[33] Bailey T, Nieto J and Guivant J et al. Consistency of the EKF-SLAM algorithm[C]. IEEE International Conference on Robots and Systems, Beijing, China, 2006: 3562~3568.
[34] Crouse D F, Willett P and Bar-Shalom Y. A low-complexity sliding-window Kalman FIR smoother for discrete-time models[J]. IEEE Signal Processing Letters, 2010, 17(2): 177~180.
[35]张贤达.矩阵分析与应用[M].北京:清华大学出版社, 2004.
[36] Villien C, Ostertag P. A polynomial approximation algorithm for real-time maximum-likelihood estimation[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2082~2095.
[37]刘吉英.基于特征信息建模的高精度目标定位[D].长沙:国防科学技术大学, 2006.
[38] Ramsay J O , Silverman B W. Functional data analysis(Second Edition)[M]. Springer Science, 2005.
[39] Zhang D W and Lin X H. Variance component testing in generalized linear mixed models for longitudinal/clustered data and other related topics[J]. Lecture Notes in Statistics, 2008, 192, Part I: 19~36.
[40] Fan J Q, Lin X H and Lu J S. New developments in biostatistics and bioinformatics[M]. Beijing: Highe Education Press, 2009.
[41] Lu W, Andera R and Lin X H. Nonparametric regression with missing outcomes using weighted kernel estimating equations[J]. Journal of the Ameerican Statistical Associaltion, 2010, 105(491): 1135~1146.
[42] Zhang S Y, Lin X H. Nonparmetric regession using local kernel estimation equations for correlated filure time data[J]. Oxford Journals Mathematics&Physical Sciences Biometrika. 2008, 95(1): 123~137.
[43] Lin X H and Zhang D W. Inference ingenralized additive mixed models by using smoothing splines[J]. J R Statist Soc B, 1999, 61: 381~400.
[44] Li Y S, Lin X H and Peter M. Bayesian inference in semoiparametric mixed models for longitudinal data[J]. Biometerics Journal of the International Biometric Society, 2010, 66(1): 70~78.
[45] Engle R F, Granger C W J and Rice J et al. Semiparametric estimates of the relation between weather and electricity sales[J]. JASA, 1986(81): 310~320.
[46]邱卫宁,陶本藻,姚宜斌等.测量数据处理理论与方法[M].武汉:武汉大学出版社, 2008.
[47]潘晓刚,李济生,段晓君等.天基空间目标监视与跟踪系统轨道确定技术研究[J].自然科学进展, 2008, 18(11): 1226~1239.
[48]潘旺华,文援兰,廖瑛.基于半参数回归模型的批处理确定卫星轨道方法[J].宇航学报, 2008. 29(6): 1917~1921, 1954.
[49]潘雄,半参数模型的估计理论及其应用[D].武汉:武汉大学, 2005.
[50]周宏仁等.机动目标跟踪[M].北京:国防工业出版社, 1991.
[51] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part IV: decision-based methods[C]. Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, Orlando, FL, USA, April 2002(4728-60): 1~24.
[52] Ru J, Jilkov V P, and Li X R et al. Sequential detection of target maneuvers[C]. 7th International Conference on Information Fusion (FUSION), 2005: 345~351.
[53] Nikiforov I V. A lower bound for the detection/isolation delay in a class of sequential tests[J]. IEEE Transactions on Information Therory, 2003, 49(11): 3037~3047.
[54] Veeravalli V V. Decentralized quickest change detection[C]. IEEE IEEE Transactions on Information Therory, 2001, 47(4): 1657~1665.
[55] Shi J F, Q L and He C W. A maneuvering target detecting method based on lifting wavelet transform algorithm[C]. CIS2008, 2008: 710~713.
[56] Ji H B, Song L P and Gao X b. Higher-order cumulants based maneuver detection in Kalman Filter[C]. Proceedings of IEEE TENCON’02, 2002: 1385~1388.
[57] Duh F B and Lin C T. Tracking a maneuvering target using neural fuzzy network[J]. IEEE Transanctions on Systmens, Man and Cybernietics—Part B: Cybernatics, 2004, 34(1): 16~33.
[58] Bahari M H, Karsaz A and Khaloozadeh H. New algorithm based on combined fuzzy logic and Kalman filter for target maneuver detection[C]. The 1st International Symposium on systems and control in aerospace and astronautics, 2006: 520~524.
[59]杨元喜,文援兰.卫星精密轨道综合自适应抗差滤波技术[J].中国科学D辑, 2003, 3(11): 1112~1119.
[60] Zheng D W, Zhong P and Ding X L et al. Filtering GPS time-series using a Vondrak filter and gross-validation[J]. Journal of Geodesy, 2005, 79(6): 363~369.
[61]谷德峰等.抗差Vondrak滤波器设计及其在粗差探测中的应用[J].兵工学报, 2008, 29(6):696~700.
[62] Blom H A P and Bar-shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Transactionson Automatic Control, 1988, AC-33(8): 780~783.
[63] Li X R and Jilkov V P. A survey of maneuvering target tracking—Part V:multiple-moel methods[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003(11): 1~57.
[64] Liu J S and Lin R H. A Hierarchical adaptive interacting multiple model algorithm[C]. 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007: 699~703.
[65]段琢华,蔡自兴,丁金霞.不完备多模型混合系统故障诊断的粒子滤波算法[J].自动化学报, 2008, 34(5): 581~587.
[66] Hamed S G and Mohammad B M. A Systematic fuzzy decision-making process to choose the best model among a set of competing models[J]. IEEE Transanctions on Systmens, Man and Cybernietics—Part A: Systems and Humans, 2008, 38(5): 1118~1128.
[67]潘泉,杨峰,叶亮等.一类非线性滤波器——UKF综述[J].控制理论与决策, 2005, 20(5): 481~489.
[68] Julier S J and Uhlmann J K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations[C]. Proceedings of the American Control Conference, Jefferson City, 2002: 887~892.
[69] Farina A and Ristic B. Tracking a ballistic target: comparison of several nonlinear filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(3): 854~867.
[70]柴霖,袁建平.非线性估计理论的最新进展[J].宇航学报, 2005, 26(3): 380~384.
[71] Schei T S. A finite difference method for linearization in nonlinear estaimation algorithms[J]. Automatica, 1997, 33(11): 2051~2058.
[72] N?rgaard M, Poulsen N K and Ravn O. New developments in state estimation for nonlinear system [J]. Automatica, 2000, 36(11): 1627~1638.
[73] Simon Maskell, Neil Gordon. A tutorial on particle filters for on-line nonlinear/non-Guasian bayesian tracking[J]. IEEE Transaction on Signal Processing, 2001: 1~14.
[74]张红梅,邓正隆,林玉荣.一种基于模型误差的UKF方法[J].航空学报, 2004, 25(6): 598~601.
[75]何秀凤,杨光.扩展区间Kalman滤波器及其在GPS/INS组合导航中的应用[J].测绘学报, 2004, 33(1): 47~52.
[76]杨小军.粒子滤波进展与展望[J].控制与决策, 2006, 23(2): 262~267.
[77] Rudolph der Merwe Doucet A, and de Freitas N et al.The unscented particle filter [C]. Advances in Neural Information Proc Systems (NIPS13), 2004: 1~7.
[78] Li S J, Wang H and Chai TY. A t-distribution based particle filter for target tracking[C].Proceedings of the 2006 American Control Conference, Minneapolis,Minnesota, USA, June 14-16, 2006: 2191~2196.
[79] Oswald L. Approximate bayesian multi-body tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1436~1449.
[80] Julier S J and Uhlmann J K. A new extension of the Kalman filter to nonlinear systems[C]. The Proceedings of AeroSense: 11th International Symposium Aerospace/Defense Sensing, Simulation and Controls, Orlando, 1997: 54~65.
[81] Harini. Deterministic sampling-based switching Kalman filtering for vehicle tracking[C]. IEEE Intelligent Transportation Systems Conference 2006, Toronto, Canada, September, 2006: 17~20.
[82] Ge X G, Jiang J and Ma X Y. Multiple moving platforms and multi-sensor maneuvering target tracking based on fuzzy adaptive UKF[C]. IMACS Multiconference on Computational Engineering in Systems Applications(CESA), Beijing, China, 2006: 2015~2020.
[83] Zhan R H and Wan J W. Neural network-aided adaptive unscented Kalman Filter for nonlinear state estimation[J]. IEEE Signal Processing Letters, 2006, 13(7): 445~448.
[84] Ananthasayanam M R, Sarkar A K and Bhattacharya A et al. Nonlinear observer state estimation from seeker measurements and seeker-radar measurements fusion[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit 15-18 August 2005, San Francisco, California, 2005: 1~25.
[85] Daum F. Nonlinear filters: beyond the Kalman filter[J]. IEEE Aerospace and Electronics System Magazine, 2005, 20(8): 57~69.
[86] Wu Y X, Hu D W and Wu M P et al. Unscented Kalman filtering for additive noise case: augmented versus nonaugmented[J]. IEEE Signal Processing Letters, 2005, 12(5): 357~360.
[87] Wang E A and Merwe R. The unscented Kalman filter, in Kalman filtering and neural networks[M]. S. Haykin, Ed. N York: Wiley Pub, 2001: 221~280.
[88] Hao Y, Xiong Z and Sun F et al. Comparison of unscented Kalman filters[C]. Proceedings of the 2007 IEEE Intern Conference on Mechanic and Automatic, Harbin, China, 2007: 895~899.
[89] Li H, Xu D, Jun J et al. Sequence Unscented Kalman filtering algorithm[C]. The 3rd IEEE Conference on Industry and Electronic Application, 2008 (ICIEA 2008): 1374~1378.
[90] Xu J, Jing Y and Georgi M et al. Two-stage unscented Kalman filter for nonlinear systems in the Presence of unknown random bias[C]. 2008 American Control Conference, Seattle, Washington, USA, 2008: 3530~3535.
[91]钟慧敏,房建成.基于非线性模型预测滤波的微纳卫星定位方法[J].北京航空航天大学学报, 2008, 34(11): 1339~1342.
[92] van der Merwe R. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models[D]. Oregen: Oregon Health & Science University, 2004.
[93] Mookerjee P and Reifler F. Reduced state estimator for systems with aparametric inputs[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(2): 446~461.
[94] Haessig D and Friedland B. Separate-bias estimation with reduced-order Kalman filters[J]. IEEE Transactions on Automatic, 1998, 43(7): 983~987.
[95] Mariani S and Corigliano A. Impact induced composite delamination: state and parameter identification via joint and dual extended Kalman filters[J]. Computer Methods in Applied mechanics and engineering, 2005, 194: 5242~5272.
[96] Moradkhani H, Sorooshian S and Gupta H V et al. Dual state–parameter estimation of hydrological models using ensemble Kalman filter[J]. Advances in Water Resources, 2005, 28: 135~147.
[97] Kim, K H, Lee J G and Park C G. Adaptive two-stage extended Kalman filter for a fault-tolerant INS-GPS loosely coupled system[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(1): 125~137.
[98] Lange K A. Gradient algorithm locally equivalent to the EM algorithm[J]. Journal of the Royal Statistical Society, Series B, 1995, 59(2): 425~437.
[99] Golub G and Pereyra V. Separable nonlinear least squares: the variable projection method and its applications[J]. Inverse Problems, 2003, 19: 1~26.
[100] Wan E A, van der Merwe R and Nelson A T. Dual estimation and the unscented transformation, in neural information processing systems[M]. Massachusetts: MIT press, 2000: 666~672.
[101] Ruhe A and Wedin P A. Algorithms for separable nonlinear least squares problems[J]. SIAM Review, 1980, 22: 318~337.
[102] Campillo F and Rossi V. Convolution particle filter for parameter estimation in general state-space models[J]. IEEE Transactions on Aerospace and Electronic Systems, 45, 2009, 3: 1063~1072.
[103] Wan E A and Nelson A T. Neural dual extended Kalman filtering: applications in speech enhancement and monuaral blind signal separation[C]. In IEEE Workshop on Neural Networks for Signal Processing, 1997: 466~475.
[104] Gannot S and Moonen M. On the application of the unscented Kalman filter to speech processing[C]. Proc. Int. Workshop Acoust. Echo Noise Control (IWAENC), 2003: 1~4.
[105] Tian X J, Xie Z H and Dai A G. A land surface soil moisture data assimilation system based on the dual-UKF method and the community land model[J]. Journal of Geophysical Research, 2008, 113(D14127): 1~11.
[106] Jamoos A, Grivel E and Shakarneh N et al. Dual H∞vs dual Kalman filters forM-AR parameter estimation form noisy observations[C]. 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland. 2009: 1700~1704.
[107] He F and Wu L. Sequential dual filter based smoothing framework for integrated navigation systems[C]. Instrumentation and Measurement Technology Conference Proceedings (2007IMTC), Warsaw, Poland, 2007: 1~5.
[108] Gove J H and Hollinger D Y. Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface-atmosphere exchange[J]. Journal of Geophysical Research, 2006, 111(D08S07): 1~21.
[109] Hegyi A, Girimonte D and Babuska et al. A comparison of filter configurations for freeway traffic state estimation[C]. 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada. 2006: 1029~1034.
[110] Zhang H and Shahriar N. EKF-based recursive dual estimation of structure and motion from stereo data[J]. IEEE Journal of Oceanic Engineering, 2010, 35(2): 424~437.
[111] Wan E A and Nelson A. T Dual Kalman filtering methods for nonlinear prediction, estimation and smoothing[C]. In Advances in Neural Information Processing Systems, 1997, 9: 1~7.
[112] Nelson A T and Wan E A. A two-observation Kalman framework for maximum-likelihood modeling of noisy time series[C]. The 1998 IEEE International Joint Conference on Neural Networks Proceedings, IEEE World Congress on Computational Intelligence, 1998: 2489~2494.
[113] Jiang Z, Song Q and He Y Q et al. A novel adaptive unscented Kalman filter for nonlinear estimation[C]. Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, 2007: 4293~4298.
[114] Soken H E and Hajiyev C. Pico satellite attitude estimation via robust unscented Kalman filter in the presence of measurement faults[J]. ISA Transactions, 2010: 249~256.
[115] Durovic Z M and Kovacevic B D. Robust estimation with unknown noise statistics[J]. IEEE Transactions on Automatic Control, 1999, 44(6): 1292~1296.
[116]杨元喜,张双成.导航解算中的系统误差及其协方差矩阵拟合[J].测绘学报, 2004, 33(3): 190~194.
[117]易东云,朱炬波,王正明等.多站连续波跟踪雷达的测速定轨研究[J].中国空间科学技术, 1998, 18(5): 19~24.
[118] Emmanuel J C, Michael B W. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008: 21~30.
[119] Vaswani N. Kalman filtered compressed sensing [C]. In IEEE International Conference on Image Proc(ICIP), 2008: 893~896.
[120] Vaswani N. Analyzing least squares and Kalman filtered compressed sensing[C].ICASSP 2009, 2009: 3013~3016.
[121] Qiu C, Lu W and Vaswani N. Real-time dynamic MR image reconstruction using Kalman filtered compressed sensing[C]. In ICASSP 2009, 2009: 393~396.
[122]胡绍林,孙国基.靶场外测数据野值点的统计诊断技术[J].宇航学报, 1999(2): 68~74.
[123]肖艳军,李建勋.抗野值多速率模型及交互式多传感器状态融合[J].自然科学进展, 2005, 15(9): 1106~1112.
[124]张磊,汪渤.抗野值H∞滤波在组合导航中的应用[J].北京理工大学学报, 2009, 29(7): 600~604.
[125]杨元喜. Kalman滤波异常误差检测[J].测绘科学与工程, 2005, 25(4): 1~4.
[126]荣思远,穆荣军,崔乃刚. EKF容错滤波方法在磁测自主导航中的应用研究[J].电子学报, 2006, 34(12): 2268~2271.
[127]陈轲,归庆明,柳丽等. Gauss-Markov模型的t型抗差估计[J].测绘学报, 2008, 37(3): 281~284, 292.
[128] Huber P J. Robust statistics[M]. New York: John Wiley&Sons, 1981.
[129] Ting J, Theodorou E and Schaal S. A Kalman filter for robust outlier detection[C]. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems San Diego, CA, USA, 2007: 1514~1519.
[130] Ou J K. A new method of identifying and locating gross errors—quasi-accurate detection[J]. Chinese Sciences Bulletin, 1999, 44(10): 1777~1781.
[131]柴艳菊,欧吉坤. Kalman滤波质量控制的一种改进算法[J].自然科学进展, 2004, 14(8): 904~909.
[132] Martin R D, Yohai V J. Influence functional for time series[J]. Ann Statist, 1986, 14(3): 781~819.
[133] Barner K E and Aysal T C. Polynomial weighted median filtering[J]. IEEE Transactions on Signal Processing, 2006, 54(2): 636~650.
[134] Lee H and Roberts S J. On-line novelty detection using the Kalman filter and extreme value theory[C]. 19th International Conference on Pattern Recognition, 2008: 1~4.
[135] Atanassov R, Bose P and Couture M et al. Algorithms for optimal outlier removal[J]. Journal of Discrete Algorithms, 2009(7): 239~248.
[136] Maiz C S, Miguez J and Djuric P M. Particle filtering in the presence of outliers[C]. IEEE/SP 15th Workshop on Statistical Signal Processing, 2009: 33~36.
[137] Zuo Y J, Cui H J. Statistical depth functions and some applications[J]. Advances in mathematics, 2004, 33(1): 1~25.
[138] Sara L P, Juan R. Depth-based inference for functional data[J]. ComputationalStatistics & Data Analysis, 2007, 51: 4957~4968.
[139]潘晓刚,周海银.卫星轨道确定模型误差的深度加权核估计方法[J].天文学报, 2009, 50(1): 85~97.
[140]李强,吴翊.ρ混合样本线性模型M估计的强相合性[J].应用数学, 2007, 20(3): 381~385.
[141]欧吉坤.测量数据的质量控制理论探讨[J].测绘工程, 2001, 10(2): 6~10.
[142]朱炬波.不完全测量数据的建模、处理及应用[D].长沙:国防科技大学, 2004.
[143]吴旭光.水下自主航行器动力学模型——建模和参数估计[M].西安:西北工业大学出版社, 1998.
[144] Svehla D and Rothacher M. Kinematic and reduced-dynamic precise orbit determination of low earth orbiters[J]. Advances in Geosciences, 2003, 1: 47~56.
[145] Dankovic B, Antic D, Jovanovic Z et al. Systems modeling based on Legendre polynomials[C]. The 5th International Symposium on Applied Computational Intelligence and Informatics, Timi?oara, Romania, 2009: 241~146.
[146] Chen Z. Local observability and its application to multiple measurement estimation[J]. IEEE Transactions on Industrial Electronics, 1991, 38(6): 491~496.
[147]周卫东,乔相伟,吉宇人等.基于新息和残差的自适应UKF算法[J].宇航学报, 2010, 31(7): 1798~1804.
[148] Li X R. Kalman filter versus IMM estimator: when do we need the latter? [J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1452~1457.
[149]范小军,刘锋.一种新的机动目标跟踪的多模型算法[J].电子与信息学报, 2007, 29(3): 532~535.
[150] Julier S J and Laviola J J. On Kalman filtering with nonlinear equality constraints[J]. IEEE Transations on Signal Processing, 2007, 55(6): 2774~2784.
[151]孙海燕,潘雄.正规化矩阵正定时半参数估计量的统计性质[J].测绘学报, 2004, 33(3): 228~232.
[152] Briers M, Maskell S R and Wright R. A rao-blackwellised unscented Kalman filter[C]. Proceedings of the Sixth Int Conference of Information Fusion, Cairns Australia 2003: 1~7.
[153] Sch?n T, Gustafsson F and Nordlund P. Marginalized particle filters for mixed linear/nonlinear state-space models[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2279~2289.
[154] Kol S, Fossa B A and Scheic T S. Constrained nonlinear state estimation based on the UKF approach[J]. Computers and Chemical Engineering, 2009(33):1386~1401.
[155] Tian X and Bar-Shalom Y. Coordinate conversion and tracking for very long range radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 1073~1088.
[156] Xu J, Georgi M D and Yuan W J et al. UKF design and stability for nonlinear stochastic systems with correlated noises[C]. Proceedings of the 46th IEEE Conference of Decision and Control, New Orleans, LA, USA, 2007: 6226~6231.
[157] Hegyi A, Girimonte D and Babuska R et al. A comparison of filter configurations for freeway traffic state estimation[C]. 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada, 2006: 1029~1034.
[158]李强.单星对卫星目标的被动定轨与跟踪关键技术研究[D].长沙:国防科学技术大学, 2007.
[159] Mackey M and Glass L. Oscillations and chaos in physiological control systems[J]. Science, 1997, 4300: 287~289.
[160] van der Merwe R. The ReBEL toolkit website. Available: http://choosh.bme.ogi.edu/rebel.
[161]赵汉元编著.飞行器再入动力学和制导[M].长沙:国防科技大学出版社, 1997.
[162]周宏潮,王正明.线性模型信号估计的精度分析[J].宇航学报, 2005, 26(增): 145~148.
[163]陶本藻,施闯,姚宜斌.顾及系统误差的平差模型研究[J].测绘学院学报, 2002, 19(2): 79~81.
[164]曹轶之,隋立芬,范澎湃.一种Kalman滤波系统误差及其协方差矩阵的半参数估计方法[J].测绘科学, 2009, 34(2): 64~66,84.
[165]赵德勇,王正明,潘晓刚等.基于半参数回归的联合定轨误差估计仿真算法[J].系统仿真学报, 2007, 19(8): 1692~1695.
[166] Candes E J, Romberg J and Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transations on Information Theory, 2006, 52(2): 489~509.
[167] Carmi A, Gurfil P and Kanevsky D. Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms[J]. IEEE Transactions on Signal Processing, 2010, 58(4): 2405~2409.
[168] Shamaiah M and Vikalo H. Rao-Blackwellized unsented Kalman filter for nonlinear systems with bandwidth constraints[C]. ICASSP 2010, 2010: 2642~2645.
[169] James G M, Radchenko P and Lv J. DASSO: Connections between the dantzig selector and LASSO[J]. Journel of the Royal Satatistical Society Series B(Methodological), 2007, 55(6): 2774~2784.
[170]周卫东,乔相伟,吉宇人等.基于新息和残差的自适应UKF算法[J].宇航学报, 2010, 31(7): 1798~1804.
[171] Morales Y, Takeuchi E and Tsubouchi T. Vehicle localization in outdoor woodland environments with sensor fault detection[C]. 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 2008: 449~454.
[172] Cetin M. Robust model selection criteria for robust Liu estimator[J]. European Journal of Operational Research, 2009: 21~24.
[173] Zuo Y J, Cui H J, He X M. On the stahel-Donoho estimator and depth-weighted means of multivariate data[J]. Ann Statist, 2004, 32(1): 483~499.
[174] Bar-Shalom Y. Update with out-of-sequence measurements in tracking: exact solution[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(3): 769~777.
[175] Julier S J. The spherical simplex unscented transformation[C]. Proceedings of the American Control Conference, Denver, 2003: 2430~2434.
[176] Julier S J. The scaled unscented transformation[C]. Proceedings of the American Control Conference, Jefferson City, 2002: 4555~4559.