GPS/SINS超紧密组合导航系统的关键技术研究
|
摘要
GPS/SINS超紧密组合导航系统增强了对GPS的修正,利用组合导航滤波器估计的导航信息辅助GPS的信号跟踪过程,增强了GPS的抗干扰能力,增强了组合系统在弱信号和高动态环境下的导航性能。
本文主要对GPS/SINS超紧密组合导航系统的GPS修正、可观测性分析、辅助信息异常检测和导航数据同步、组合导航滤波这些关键问题进行了研究和探讨。
目前超紧密组合导航系统GPS修正方法的组合导航滤波器计算量大,系统可靠性差,GPS跟踪过程容易受到不稳定跟踪环路的影响,针对这些问题本文提出了双跟踪闭环超紧密组合导航系统GPS修正方法。双跟踪闭环修正方法通过增强GPS跟踪环的跟踪性能来提高GPS的性能,GPS的跟踪过程以自主跟踪为主,由SINS输出的导航信息对跟踪信号的多普勒频移进行估计,提供对GPS跟踪过程的外部修正。跟踪环内码与载波跟踪一同进行,相互辅助,利用码跟踪较好的抗干扰和载波跟踪的高精度特性,增强了GPS的自主跟踪性能。组合导航滤波器测量GPS与SINS的导航偏差,实时的估计、修正SINS的误差,修正后的SINS为GPS跟踪过程提供了高更新率和可靠的修正信息,增强了跟踪过程的抗干扰能力。由于各通道的跟踪过程没有直接的联系,减小了跟踪过程的相互干扰,增强了在弱信号环境下的跟踪性能。载波频率误差修正与载波相位误差修正分离,分别由通道滤波器和组合导航滤波器估计、修正,减小了组合导航滤波器的计算量。仿真结果表明,双跟踪闭环修正方法增强了GPS跟踪过程在弱信号和载体高动态运动环境下的性能,满足提高超紧密组合导航系统性能的要求。
可观测性分析是超紧密组合导航系统设计必不可少的一部分。由于超紧密组合导航系统复杂的时变特性,应用一些传统的可观测分析方法对其可观测性进行分析较为困难。本文通过对超紧密组合导航系统的分析,从可观测性的定义出发,根据系统的特点提出了一种超紧密组合导航系统可观测性分析的方法。根据系统的特性,通过一系列降维和近似处理简化了可观测性的分析过程,将载体的运动分解,通过对分解运动的可观测性分析来了解组合系统的可观测性随载体运动变化的情况,为系统的设计提供了指导。
提出了一种基于局域波分析的多普勒频移异常检测方法。针对经验模式分解方法筛选条件不严格和分解存在伪分量的问题,改进了筛选条件,提出了伪分量的消除方法。利用多普勒频移信号所具有的自相似特性,应用局域波方法对多普勒频移与同相信号在相同时刻自相似性的变化进行分析,得出多普勒频移异常的判断。实验分析结果表明,此方法可以有效的检测出多普勒频移所出现的异常。以AR模型估计为基础,提出了SINS辅助信息时序估计方法,有效地抑制了短时SINS修正扰动。提出了以GPS秒输出脉冲为时间基准的GPS与SINS的时间同步方法。针对通道内滤波过程、组合导航滤波过程的观测时刻与滤波时刻不同步问题,提出了数据同步方法,根据数据更新同步时间差,分别由通道滤波器和导航滤波器所得到的最优估计值,利用时间更新和状态更新的方法对滤波时刻的观测值进行估计,减小滤波误差。仿真实验表明,本文所提出的数据同步方法有效的减小了时序误差,增强了GPS跟踪过程和组合导航滤波过程的精度。
组合导航参数估计是组合导航系统研究的最关键问题。针对滤波过程因误差模型不正确和噪声估计不准确导致滤波估计误差较大的问题,通过对超紧密组合导航系统的研究,提出了以最大似然估计为基础的系统噪声和量测噪声的估计方法。对超紧密组合导航系统的可观测性分析表明,系统中存在不可观测量,且随着载体的运动状态的不同可观测性将发生变化,状态的不可观测持续时间一般较短。状态可观测性的改变将对组合导航滤波过程产生很大影响,导致滤波误差增大甚至发散。本文提出了以模糊控制规则为基础的渐消记忆自适应卡尔曼滤波方法。该方法对卡尔曼滤波的增益矩阵设置了权值,通过改变权值来调整观测信息对新状态估计所产生的影响,由前面对超紧密组合导航系统可观性的分析结果,设计了权值的模糊调整方法。由载体的速度、加速度和姿态测量信息,由已得到的可观测性分析结果,实时调整权值的大小,减小不可观测状态对滤波过程的影响。仿真实验结果表明,模糊渐消记忆自适应卡尔曼滤波方法减小了状态可观测性短暂变化的影响,增强了滤波的精度,提高了超紧密组合导航系统的性能。
Ultra-tightly integrated GPS/SINS navigation system enhances the GPS correction, use the estimation of the navigation data to aid the process of GPS signal tracking, which enhances the anti-jamming capability and the navigation performance of the integrated system in the weak signal and high-dynamic environment.
The filter has large amount of calculation with GPS corrected algorithm for ultra-tightly integrated GPS/SINS navigation system, GPS tracking process vulnerable to the impact of unstable tracking loop. To address these issues, this paper proposes a dual-track and closed-loop GPS correction for ultra-tightly integrated GPS/SINS navigation system. The dual-track and closed-loop correction algorithm improve GPS performance by enhancing the tracking performance of GPS tracking loop. GPS mainly track the signal independently, from the SINS output navigation information to estimate the Doppler frequency of the tracking signal, providing GPS tracking process with external amendments. Within the tracking loop, the code and carrier track together for mutual support. Use a good anti-interference features of code tracking and high-precision features of carrier tracking, which enhanced GPS autonomous tracking performance. The integration navigation filter measurements with the SINS navigation deviation, real-time estimate and correct the SINS errors, corrected SINS provide high update rate and reliable information to enhance the anti-jamming capability for GPS tracking. As a result of channels of the tracking loop are not directly connect, which reduce the mutual interference of the tracking process and enhance the tracking performance in weak signal. The correction of the carrier frequency and phase tracking error are separated, the channel filter and integrated filter estimate and correct the errors, which reduce the load. The simulation results show that the dual-track and closed-loop corrected algorithm enhance the performance of the GPS tracking process in weak signal and high dynamic environments, which meet the requirements which increased the performance of the ultra-tightly integrated navigation system.
Observability analysis is important for ultra-tightly integrated navigation system design. Use the traditional method to analysis the observability of the ultra-tightly integrated navigation system is difficult, because of the complex time varing characteristics. Form the definition of the observability, this paper proposes a observability analysis method for ultra-tightly integrated navigation system by analyzing the ultra-tight integrated navigation system. According to the characteristics of the system, through a series of approximation, which simplifies the process of observability analysis. Through the decomposition of the movement and the observability results understand the information that the observability variation with the body movement, which provide guidance for system design.
A detection method of abnormal Doppler frequency based on local wave analysis is proposed. According to the problem that the empirical mode decomposition method are not strict and existing pseudo-component, improves selection conditions, proposes the elimination of pseudo-component method. Using Doppler frequency shift signal with the self-similar characteristics, analyzes the changes of self-similarity in the same time between Doppler frequency and in-phase signal with local wave method, obtains judgments of abnormal Doppler. Experimental results show that this method can effectively detect the Doppler shift arising from abnormal. Based on the AR model, this paper proposes SINS supporting information of timing estimation method, effectively suppress the short-term disturbance. A time synchronization method between GPS and SINS is proposed based on the GPS second pulse. According to the problem that observation time data and update synchronization time are different by channel filtering process and integrated navigation filtering process, this paper proposes a data synchronization method. Use the time and data update method to estimate the observations in filtering time by the optimal estimation of the channel filter and integrated navigation filter output, reducing the filtering error. The simulation results show that the algorithm proposed by this paper effectively reduce the time warp and enhance the accuracy of GPS tracking process and integration filtering process.
Navigation parameter estimation is most important issue for integrated system. According to the problem which incorrect error model and inaccurate noise estimation lead to larger filter error. Through study the ultra-tightly integrated navigation system, the estimation method of system noise and measurement noise based on the maximum likelihood estimation is proposed. Analyzing the observability of the ultra-tightly integrated navigation system, which results show that there have unobservable state within system, this state change with the movement of body , this unobservability generally much shorter. The variation of state observability have a significant impact to integrated navigation system, leading to increased filtering error or divergence. This paper proposes a adaptive fading memory kalman filtering method based on fuzzy rules. This method sets a weight for kalman filter gain matrix, by changing the weight to adjust the impact from observable information to estimate the new state. Based on the observability analysis results for ultra-tightly integrated navigation system in front, designing the method for adjusting the weights. From the velocity, accelerate, attitude information and observability analysis results, adjusting the weight in real time, which reduce the impact from unobservable state to filtering process. The simulation results show that the fuzzy fading memory adaptive kalman filtering method reduce the impact of short-time change of state observability, enhance the accuracy of filtering and performance of ultra-tightly integrated navigation system.
本文主要对GPS/SINS超紧密组合导航系统的GPS修正、可观测性分析、辅助信息异常检测和导航数据同步、组合导航滤波这些关键问题进行了研究和探讨。
目前超紧密组合导航系统GPS修正方法的组合导航滤波器计算量大,系统可靠性差,GPS跟踪过程容易受到不稳定跟踪环路的影响,针对这些问题本文提出了双跟踪闭环超紧密组合导航系统GPS修正方法。双跟踪闭环修正方法通过增强GPS跟踪环的跟踪性能来提高GPS的性能,GPS的跟踪过程以自主跟踪为主,由SINS输出的导航信息对跟踪信号的多普勒频移进行估计,提供对GPS跟踪过程的外部修正。跟踪环内码与载波跟踪一同进行,相互辅助,利用码跟踪较好的抗干扰和载波跟踪的高精度特性,增强了GPS的自主跟踪性能。组合导航滤波器测量GPS与SINS的导航偏差,实时的估计、修正SINS的误差,修正后的SINS为GPS跟踪过程提供了高更新率和可靠的修正信息,增强了跟踪过程的抗干扰能力。由于各通道的跟踪过程没有直接的联系,减小了跟踪过程的相互干扰,增强了在弱信号环境下的跟踪性能。载波频率误差修正与载波相位误差修正分离,分别由通道滤波器和组合导航滤波器估计、修正,减小了组合导航滤波器的计算量。仿真结果表明,双跟踪闭环修正方法增强了GPS跟踪过程在弱信号和载体高动态运动环境下的性能,满足提高超紧密组合导航系统性能的要求。
可观测性分析是超紧密组合导航系统设计必不可少的一部分。由于超紧密组合导航系统复杂的时变特性,应用一些传统的可观测分析方法对其可观测性进行分析较为困难。本文通过对超紧密组合导航系统的分析,从可观测性的定义出发,根据系统的特点提出了一种超紧密组合导航系统可观测性分析的方法。根据系统的特性,通过一系列降维和近似处理简化了可观测性的分析过程,将载体的运动分解,通过对分解运动的可观测性分析来了解组合系统的可观测性随载体运动变化的情况,为系统的设计提供了指导。
提出了一种基于局域波分析的多普勒频移异常检测方法。针对经验模式分解方法筛选条件不严格和分解存在伪分量的问题,改进了筛选条件,提出了伪分量的消除方法。利用多普勒频移信号所具有的自相似特性,应用局域波方法对多普勒频移与同相信号在相同时刻自相似性的变化进行分析,得出多普勒频移异常的判断。实验分析结果表明,此方法可以有效的检测出多普勒频移所出现的异常。以AR模型估计为基础,提出了SINS辅助信息时序估计方法,有效地抑制了短时SINS修正扰动。提出了以GPS秒输出脉冲为时间基准的GPS与SINS的时间同步方法。针对通道内滤波过程、组合导航滤波过程的观测时刻与滤波时刻不同步问题,提出了数据同步方法,根据数据更新同步时间差,分别由通道滤波器和导航滤波器所得到的最优估计值,利用时间更新和状态更新的方法对滤波时刻的观测值进行估计,减小滤波误差。仿真实验表明,本文所提出的数据同步方法有效的减小了时序误差,增强了GPS跟踪过程和组合导航滤波过程的精度。
组合导航参数估计是组合导航系统研究的最关键问题。针对滤波过程因误差模型不正确和噪声估计不准确导致滤波估计误差较大的问题,通过对超紧密组合导航系统的研究,提出了以最大似然估计为基础的系统噪声和量测噪声的估计方法。对超紧密组合导航系统的可观测性分析表明,系统中存在不可观测量,且随着载体的运动状态的不同可观测性将发生变化,状态的不可观测持续时间一般较短。状态可观测性的改变将对组合导航滤波过程产生很大影响,导致滤波误差增大甚至发散。本文提出了以模糊控制规则为基础的渐消记忆自适应卡尔曼滤波方法。该方法对卡尔曼滤波的增益矩阵设置了权值,通过改变权值来调整观测信息对新状态估计所产生的影响,由前面对超紧密组合导航系统可观性的分析结果,设计了权值的模糊调整方法。由载体的速度、加速度和姿态测量信息,由已得到的可观测性分析结果,实时调整权值的大小,减小不可观测状态对滤波过程的影响。仿真实验结果表明,模糊渐消记忆自适应卡尔曼滤波方法减小了状态可观测性短暂变化的影响,增强了滤波的精度,提高了超紧密组合导航系统的性能。
Ultra-tightly integrated GPS/SINS navigation system enhances the GPS correction, use the estimation of the navigation data to aid the process of GPS signal tracking, which enhances the anti-jamming capability and the navigation performance of the integrated system in the weak signal and high-dynamic environment.
The filter has large amount of calculation with GPS corrected algorithm for ultra-tightly integrated GPS/SINS navigation system, GPS tracking process vulnerable to the impact of unstable tracking loop. To address these issues, this paper proposes a dual-track and closed-loop GPS correction for ultra-tightly integrated GPS/SINS navigation system. The dual-track and closed-loop correction algorithm improve GPS performance by enhancing the tracking performance of GPS tracking loop. GPS mainly track the signal independently, from the SINS output navigation information to estimate the Doppler frequency of the tracking signal, providing GPS tracking process with external amendments. Within the tracking loop, the code and carrier track together for mutual support. Use a good anti-interference features of code tracking and high-precision features of carrier tracking, which enhanced GPS autonomous tracking performance. The integration navigation filter measurements with the SINS navigation deviation, real-time estimate and correct the SINS errors, corrected SINS provide high update rate and reliable information to enhance the anti-jamming capability for GPS tracking. As a result of channels of the tracking loop are not directly connect, which reduce the mutual interference of the tracking process and enhance the tracking performance in weak signal. The correction of the carrier frequency and phase tracking error are separated, the channel filter and integrated filter estimate and correct the errors, which reduce the load. The simulation results show that the dual-track and closed-loop corrected algorithm enhance the performance of the GPS tracking process in weak signal and high dynamic environments, which meet the requirements which increased the performance of the ultra-tightly integrated navigation system.
Observability analysis is important for ultra-tightly integrated navigation system design. Use the traditional method to analysis the observability of the ultra-tightly integrated navigation system is difficult, because of the complex time varing characteristics. Form the definition of the observability, this paper proposes a observability analysis method for ultra-tightly integrated navigation system by analyzing the ultra-tight integrated navigation system. According to the characteristics of the system, through a series of approximation, which simplifies the process of observability analysis. Through the decomposition of the movement and the observability results understand the information that the observability variation with the body movement, which provide guidance for system design.
A detection method of abnormal Doppler frequency based on local wave analysis is proposed. According to the problem that the empirical mode decomposition method are not strict and existing pseudo-component, improves selection conditions, proposes the elimination of pseudo-component method. Using Doppler frequency shift signal with the self-similar characteristics, analyzes the changes of self-similarity in the same time between Doppler frequency and in-phase signal with local wave method, obtains judgments of abnormal Doppler. Experimental results show that this method can effectively detect the Doppler shift arising from abnormal. Based on the AR model, this paper proposes SINS supporting information of timing estimation method, effectively suppress the short-term disturbance. A time synchronization method between GPS and SINS is proposed based on the GPS second pulse. According to the problem that observation time data and update synchronization time are different by channel filtering process and integrated navigation filtering process, this paper proposes a data synchronization method. Use the time and data update method to estimate the observations in filtering time by the optimal estimation of the channel filter and integrated navigation filter output, reducing the filtering error. The simulation results show that the algorithm proposed by this paper effectively reduce the time warp and enhance the accuracy of GPS tracking process and integration filtering process.
Navigation parameter estimation is most important issue for integrated system. According to the problem which incorrect error model and inaccurate noise estimation lead to larger filter error. Through study the ultra-tightly integrated navigation system, the estimation method of system noise and measurement noise based on the maximum likelihood estimation is proposed. Analyzing the observability of the ultra-tightly integrated navigation system, which results show that there have unobservable state within system, this state change with the movement of body , this unobservability generally much shorter. The variation of state observability have a significant impact to integrated navigation system, leading to increased filtering error or divergence. This paper proposes a adaptive fading memory kalman filtering method based on fuzzy rules. This method sets a weight for kalman filter gain matrix, by changing the weight to adjust the impact from observable information to estimate the new state. Based on the observability analysis results for ultra-tightly integrated navigation system in front, designing the method for adjusting the weights. From the velocity, accelerate, attitude information and observability analysis results, adjusting the weight in real time, which reduce the impact from unobservable state to filtering process. The simulation results show that the fuzzy fading memory adaptive kalman filtering method reduce the impact of short-time change of state observability, enhance the accuracy of filtering and performance of ultra-tightly integrated navigation system.
引文
[1] J.Liu,YSun,X.He,etal. Design and realization of GPS/NIS integrated nvaigation system. Proceedings of the 1997 53rd Annual Meeting of lnstiutte of Nvaigation. 1997:293-297.
[2] G.Garnes,S.Lee,A.Perez. INS/GPS space application for all flight dynamics boost through landing. PLANS , IEEE. 1996:457-463.
[3] K.Braden , C.Browning , H.Gelderloos. Integrated inertial navigation system/global positioning system for manned return vehicle autoland application. PLANS,IEEE. 1990:74-82.
[4]周露,闻新,周秀芬.组合导航制导系统的容错技术及特点.上海航天.1999,(2):58-61.
[5]董绪荣,张守信,华仲春.GPS/INS组合导航定位及其应用.长沙:国防科技大学出版社.1998:88-95.
[6]董渊文,官洪运,胡辉.GPS接收机伪码跟踪技术研究及仿真.系统仿真学报,2006,18(11):3209-3211.
[7]杨文辉.一种高动态GPS接收机跟踪环路设计.导航. 2005,41(1):45-52.
[8] M.G.Petovello , C.O.Driscoll and G.lachapelle. Weak signal carrier tracking using extended coherent integration with an ultra tight GNSS/IMU receiver. European Navigation Conference 2008. 2008:23-31.
[9] Kaplan. Understanding GPS: Principles and Applications.Boston: Artech House,2006:1-7.
[10]黄晓瑞.基于信息融合的车载组合导航系统研制.哈尔滨工业大学博士论文.2002:70-81.
[11] M.G.Petovello,D.Sun,G.Lachapelle,M.E.Cannon. Performance analysis of an ultra tightly integrated GPS and reduced IMU system. Conference of ION GNSS 2007. 2007:25-28.
[12] R.Babu and Jinling Wang. Dynamics performance of carrier and code tracking loops in ultra-tight GPS/INS/PL integration. ENC-GNSS 2005. 2005:19-26.
[13] D.Li,J.Wang,S.Babu. Enhancing the performance of ultra-tight integration of GPS/PL/INS:a federated filter approach. Symp.on GPS/GNSS. 2005: 1156-1165.
[14]程乃平,任宇飞,吕金飞.高动态扩频信号的载波跟踪技术研究.电子学报,2004,31(12):2147-2150.
[15]李小民.高动态环境GPS应用中的几个关键问题研究.北京航空航天大学博士论文,1999: 79-83.
[16]罗兴宇.基于DSP技术的高动态GPS接收机的研制.北京航空航天大学博士论文,2002: 56-72.
[17]薛文芳.高动态GPS接收机设计中几个关键问题的研究.北京航空航天大学博士论文,2002: 169-176.
[18]梁坤,施浒立.GPS接收机中码相位测量精化方法的研究.宇航学报,2007,28(3):571-575.
[19]吕艳梅,李小民,孙江生.高动态环境的GPS信号接收及其算法研究.电光与控制,2006,13(4):24-27.
[20]王伟,张廷新,史平彦.高动态环境下GPS信号跟踪算法综述.空间电子技术,2000,35(1):1-9.
[21]李小民,田庆民.适应高动态环境的GPS载波相位估计方法研究.武汉大学学报,2003,36(3):20-124.
[22] R.Babu,J.Wang. Real time data analysis of ultra tight GPS/INS integration. Symp.on GPS/GNSS(IGNSS2007). 2007:698-706.
[23] D.L,J.Wang. performance analysis of the ultra tight GPS/INS integration based on an improved Kalman filter design for tracking loops. Symp.on GPS/GNSS(IGNSS2006). 2006: 1123-1130.
[24] Wei-Lung Mao,Hen-Wai Tsao. A new fuzzy bandwidth carrier recovery system in GPS for robust phase tracking.IEEE Signal Processing Letters,2004,11(4):431-434.
[25]潘泉,于听,程咏梅,张洪才.信息融合理论的基本方法与进展.自动化学报.2003,29(4):599-615.
[26]张满生,张学庄,陈保平,许中华.新型GPS动态定位自适应卡尔曼滤波方法.中南工业大学学报,2003,34(5):543-546.
[27]柳海峰,姚郁,卢迪.Kalman滤波新息正交性抗野值法研究.电机与控制学报,2003,7(1):40-42.
[28]祝转民.Kalman滤波工程应用问题分析及改进方法研究.宇航学报.2002, 23(3):44-47.
[29] S.S.Blackman. Multiple sensor data association and fusion in aerospace application. Journal of Robot System , 1994 ,9(3):445-485.
[30] R.C.Luo.Dynamic Multi-Sensor Data Fusion System for Intelligent Robots.IEEE Journal of Robotocs and Automations. 1988, 15(4):386-396
[31] D.Whyte. Sensor models and Multisensor integration. International Journal of Robotics Research. 1988,(7):97-113.
[32] G.Kamberova. Decision-Theoretic approach to robust fusion of location Data. Journal of the Franklin Institute.1999,336(2): 269-284.
[33] L.J.Xu,Y.Z.Chen,P.Y.Cui. Improvement of D-S evidential theory in multisensor data fusion system. Fifth World Congress on Intelligent Control and Automation. 2004,4:3124-3128.
[34] H.Y.Yang,K.Park and J.G.Lee. A rotating sonar and a differential encoder data fusion for map-based dynamic positioning. Journal of Intelligent and Robotic Systems.2000,29:211-232.
[35] R.F.Gamble. Using software architecture to formally derive synchronous knowledge based systems. International Journal of Expert Systems. 1997,10(2):93-135.
[36]权太范.信息融合—神经网络-模糊推理理论与应用.北京:国防工业出版社,2002:224-275.
[37] L.Laurent,D.Renaud,K.Wojtek,S.Basel. Probabilistic and fuzzy information fusion applied to radar system ranking. Proceedings of SPIE-The International Society for Optical Engineering,2003:123-132.
[38] J.Leopoldo,S.Longhi,G.Venturini. Development and experimental validation of an adaptive extended Kalman filter for localization of mobile robots. IEEE Transactions on Robotics and Automation. 1999,15(2):219-229.
[39] T.D.Kinght. Rapid development of tightly integrated GPS/INS system. IEEE Aerospace and Electronic Systems Magazine,1997,12(4):14-19.
[40]蔡鹤皋,金明河,金峰.卡尔曼滤波与多传感器信息融合技术.模式识别与人工智能. 2003,13(3):248-253.
[41] J.Y.Liu,Y.R.Sun,X.F.He,etc. Design and realization on GPS/INS integrated navigation system. Transactions of Nanjing University of Aeronautics and Astronautics. 1998,15(2):165-171.
[42] S.Vathsal. Spacecraft attitude determination using a second order nonlinear filter. Journal of Guidance , Control , and Dynamics. 1987,10(6):559-566.
[43] M.Hassan , G.Salut , M.Singh. A decentralized computational algorithm for the global Kalman filter. IEEE Trans.Automatic Control. 1978,23:262-268.
[44] Y.M.Zhu et al. The optimality for the distributed Kalman filtering fusion with feedback. Automatica. 2001,37:1489-1493.
[45] H.Chen,T.Kirubarajan,Y.Bar-Shalom. Performance limits of Track to track fusion vs.centralized estimation. IEEE Trans.Aerospace and Electronic Systems,2003,39(3):386-398.
[46] Z.L.Deng,R.B.Qi. Multi-sensor information fusion suboptimal steady state Kalman filter. Chinese Science Abstracts,2000,6(2):183-184.
[47] N.A.Carlson. Federated square root filter for decentralized parallel processes. Proceeding of NAECON , Dayton ,1987:1448-1456.
[48]岳晓奎,袁建平.一种基于极大似然准则的自适应卡尔曼滤波算法.西北工业大学学报. 2005,23(4):469-474.
[49] A.P.Sage , G.W.Husa. Adaptive filtering with unknown prior statistics. Joint Automatic Control Conference. 1969:760-769.
[50]耿延睿,崔中兴,张洪钺,房建成.衰减因子自适应滤波及在组合导航中的应用.北京航空航天大学学报. 2004,30(5):434-437.
[51] C.Hide,T.Moore,M.Smith. Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. Symposium of Position Location and Navigation,2004,4:227-233.
[52] T.Yoshimura,T.Sueda. A Techinique for compensating the filter performance by a fictitious noise. Transactons on ASME,1978,100(2):154-156.
[53]尚捷,顾启泰. MIMS/GPS组合导航系统中卡尔曼滤波器设计与实验研究.中国惯性技术学报. 2005,13(2):45-48.
[54] J.Z.Sasiadek , Q.Wang , M.B.Zaremba. Fuzzy adaptive Kalman filtering for INS/GPS data fusion. Proceedings of the 15th IEEE International Symposium on Intelligent Control , Greece ,2000:181-186.
[55] C.Kyungho,A.Byungha,K.Hanheok. Intelligent adaptive gain adjustment and error compensation for improved tracking performance. IEICE Trans.on INF&SYST.2000,6(11): 1952-1959.
[56] R.Steven,Swanson. A fuzzy navigational state estimator for GPS/INS intergration. IEEE Symposium of Position Location and Navigation,1998: 541-548.
[57] Z.Ahmad,J.Zhang. Combination of multiple neural networks using data fusion techniques for enhanced nonlinear process Modelling. Computers and Chemical Engineering. 2005,2:295-308.
[58] A.Noureldin,R.Sharaf. INS/GPS data fusion technique utilizing radial basis functions neural networks. Position Location and Navigation Symposium. 2004,6:280-284.
[59] B.H.Yang,P.He,B.L.Wang. Research on fuzzy model and algorithms for data fusion. International Conference on Machine Learning and Cybernetics. 2003,4:2480-2484.
[60] L.Ojeda,J.Borenstein. Fuzzy logice estimation expert rule based position for mobile robots on rugged rerrain. Proceeding of the 2002 IEEE International Conference on Robotics and Automation. 2002:3170-322.
[61] S.J.Julier , J.K.Uhlmann , H.F.Durrant. A new approach for filtering nonlinear system. Proceedings of the American Control Conference,1995:1628-1632.
[62] S.J.Julier,J.K.Uhlmann,H.F.Durrant. A new method for nonlinear transformation of means and covariances in filters and estimator. IEEE Trans.Automatic Control,2000,45(3):477-482.
[63] E.Wan , V.Merwe. The unscented Kalman filter for nonlinear estimation. Proceedings of Symposium 2000 on Adaptive Systems for Signal,Communication and Control.2000:1255-1260.
[64] J.Simon,J.Jeffery,K.Uhlmann. Unscented filtering and nonlinear estimation. Proceeding of the IEEE, 2004,92(3):401-422.
[65] R.V.D.Merwe,E.A.Wan and S.Julier. Sigma point Kalman filters for nonlinear estimation and sensor fusion application to integrated navigation. AIAA Guidance,Navigation and Control Conference. 2004:425-437.
[66] J.L.Crassidis. Sigma point Kalman filtering for integrated GPS and inertial navigation. IEEE Transactions on Aerospace and Electronic Systems. 2005: 598-602.
[67] J.L.Crassidis and F.L.Markley. Unscented filtering for spacecraft attitude estimation. Journal of Guidance Control and Dynamics. 2003,45(5):910-927.
[68] A.Farina,B.Ristic and D.Benvenuti. Tracking a ballistic target: comparison of several nonlinear filters. IEEE Transactions on Aerospace and Electronic Systems. 2002,38(3):854-867.
[69] W.Li,H.Leng and Y.Zhou. Space time registration of radar and ESM using unscented Kalman filter. IEEE Transactions on Aerospace and Electronic Systems. 2004,40(3):824-836.
[70] B.Ristic,A.Farina,D.Benvenuti and M.S.Arulampalam. Performace bounds and comparison of nonlinear filters for tracking a ballistic object on re-rentry. IEEE Proceeding of Radar Sonor and Navigation. 2003,150(2):65-70.
[71] E.A.Wan and R.V.D.Merwe. The unscented Kalman filter. Kalman Filtering and Neural Networks. Haykin: Wiley Publishing. 2001,221-280.
[72] Y.C.Ho and R.C.K.Lee. A Bayesian approach to problems in stochastic estimation and control. IEEE Transactions on Automation Control. 1994,53(9):333-339.
[73] H.Carvalho,P.D.Moral. Optimal nonlinear filtering in GPS/INS integration. IEEE Transactions on AES,1997,33(3):835-849.
[74] P.J.Nordlund,F.Gustafsson. Sequential monte carlo filtering techniques applied to integrated navigation systems. Proceeding of the American Control Conference,2001:4375-4380.
[75] A.S.Babak. Approximate nonlinear filtering with applications tonavigation. Ph.D Thesis of Linkoping University. 2001:53-71.
[76] M.L.Psiaki,H.Jung. Extended Kalman filter methods for tracking weak GPS signals. Proceeding of ION GPS 2002, 2002, 2538-2553.
[77] D.M.Lin,J.B.Y.Tsui. A weak signal tracking technique for a stand-alone software GPS receiver. Proceeding of ION GPS 2002, 2002, 2534-2538.
[78] M.L.Psiaki. Smoother based GPS tracking in a software receiver. Proceeding of ION GPS 2001, 2001, 2900-2913.
[79] R.Babu, Jinling Wang and G.Rao. Analysis of ultra tight GPS/INS integrated system for navigation performance.IEEE 2008 International conference on signal processing, communication and networking, 2008, 234-237.
[80]张天光,王秀萍,王丽霞等译.捷联惯性导航技术.北京:国防工业出版社,2007:14-20.
[81]秦永元.惯性导航.北京:科学出版社,2006: 287-310.
[82]张士邈,刘放等.捷联惯导姿态算法若干问题研究.中国惯性技术学报. 2002, 10(2):1-7.
[83] M.Yu ,J.G.Lee and H.W.Park. Comparison of SDINS in flight aligment using equivalent error models. IEEE Tranasctions on Aerospace and Electronic Systems. 1999,35(3):1046-1052.
[84] Kaplan. Understanding GPS: Principles and Applications.Boston: Artech House,2006:14-46.
[85] J.Lee,B.Kim. A low noise fast lock phase locked loop with adaptive bandwidth control. IEEE Journal of Solid State Circuits. 2000,35(8):1137-1145.
[86] R.N.Crane. A simplified method for deep coupling of GPS and inertial data. Proceedings of ION NTM 2007. 2007:311-319.
[87]花俊,许斌,郝建华.高动态环境中伪码锁相环设计.装备指挥技术学院学报,2004,15(6):76-78.
[88]田明坤.高动态GPS接收机设计中几个关键问题的研究.北京航空航天大学博士论文,2002:124-137.
[89]薛鸿印.基于GPS芯片的高精度、高动态定位技术研究.哈尔滨工程大学博士论文,2001: 65-74.
[90] M.Hsieh, G. Sobelman. Clock and data recovery with adaptive loopgain for spread spectrum serdes applications. Proceedings of IEEE ISCAS 2005.2005:4883-4886.
[91] G.Egziabher. What is the difference between loose, tight, ultra-tight and deep integration strategies for INS and GNSS. GNSS Solutions. 2007, 2(1): 28-33.
[92] B.W. Parkinson, J.J. Spilker. GPS and inertial integration. American Institute of Aeronautics and Astronautics. 1996:187-218.
[93] O.S.Salychev , V.V.Voronov. Low cost INS/GPS integration: concepts and testing. Inttitute of Navigation National Technical Meeting,2000:26-28.
[94] J. D.Gautier,B. W. Parkinson and D. Gebre-Egziabher.Using the GPS/INSgeneralized evaluation tool (GIGET) for the comparison of loosely coupled, tightly coupled and ultra-tightly coupled integrated navigation systems. Proceedings of ION 59th Annual Meeting,2003: 23-25.
[95] W.D.Ding , J.L.Wang and C.Rizos. Improving adaptive Kalman estimation in GPS/INS integration. Journal of Navigation. 2007, 60(1):517-529.
[96] P.Daum, J. Beyer, T. K?hler. Aided inertial land navigation system with a minimum set of inertial sensors. Proceedings of IEEE-IEE Vehicle Navigation and Information Systems Conference.1994:284-291.
[97] S.Godha,M.G.Petovello and G.Lachapelle. Performance analysis of MEMS IMU/HSGPS/magnetic sensor integrated system in urban canyons. ION GNSS 2005,2005:13-16.
[98] Godha S. and M. E. Cannon.Integration of DGPS with a low cost MEMS based inertial measurement unit (IMU) for land vehicle navigation application. Proceedings of ION GNSS 2005 ,2005:333-345.
[99] W.Hamid, T.Abdelazim, N.E.Sheimy. Improvement of MEMS-IMU/GPS performance using fuzzy modeling. GPS Solutions , 2005 , 10:1046-1052.
[100] S.Nassar,X.Niu and N.E.Sheimy. Land vehicle INS/GPS accuratepositioning during GPS signal blockage periods. Journal of Surveying Engineering. 2005,26(12):365-372.
[101] X.Niu,S.Nassar and N.E.Sheimy. An accurate land vehicle MEMS IMU/GPS navigation system using 3D auxiliary velocity updates. Navigation. 2005,28(3):562-571.
[102] D.Li,J.Wang.Performacne analysis of the ultra-tight GPS/INS integrtation based on an improved Kalman filter design for tracking loops. International Global Navigation Satellite Systems Society. 2006:17-21.
[103] M.Petovello,G.Lachapelle. Comparison of vector-based software receiver implementations with application to ultra tight GPS/INS integration. Proceeding of GNSS 2006. 2006: 56-72.
[104] G.Egziabher, A. Razavi, P. Enge, J.D. Gautier, S. Pullen, B. Pervan, D. Akos. Sensitivity and performance analysis of doppler-aided GPS carrier tracking loops. Navigation, 2005,52(2): 49-60.
[105] Ravindra B, Wang J L, Gottapu R. Analysis of ultra-tight GPS/INS integrated system for navigation performance. IEEE 2008 International conference on signal processing. 2008:234-237.
[106] Pnay T R, Kaniuth B E. Deep Integration of navigation solution and signal processing. Proceeding of ION GNSS 2005. 2005:1095-1102.
[107] M.G.Petovello , C.O.Driscoll , G.Lachapelle. Carrier phase tracking of weak signals using different receiver architectures. Conference of ION NTM 2008. 2008:28-30.
[108] M.G.Petovello , G.Lachapelle. Comparison of vector based software receiver implementations with application to ultra tight GPS/SINS integration. Conference of ION GNSS 2006. 2006:26-29.
[109]陈军,潘高峰. GPS软件接收机基础.北京:电子工业出版社,2007:58-59.
[110] M.Wei and K.P.Schwarz. A strapdown inertial algorithm using and earth-fixed Cartesian frame. Navigation. 1990,2(37):153:167.
[111] Y.F.Jiang and P.Y.Lin. Error estimation of INS groud alignmentthrouth observability analysis. IEEE Transactions on Aerospace and Elictronic System. 1992:28(1),92-97.
[112] Y.F.Jiang and Y.P.Lin. Error estimation of INS ground alignment through observability analysis. IEEE Transactions on Aerospace and Electronic Systems. 1992,28(1):92-97.
[113]付梦印,邓志红,张继伟. Kalman滤波理论及其在导航系统中的应用.北京:科学出版社. 2003:8-32.
[114]邓自立.卡尔曼滤波与维纳滤波.哈尔滨工业大学出版社. 2001:102-160.
[115]姜军,杨亚非. SINS/GPS组合导航系统初始对准的可观测度分析.哈尔滨工业大学学报.2007,39(7):1025-1029.
[116] D.G.Meskin,I.Y.Bar-Itzhack. Observability analysis of piece wise constant systems with application to inertial navigation. Proceedings of th 29th conference on Dectation and Control. 1990:821-826.
[117]胡寿松等著.自动控制原理.第四版:科学出版社,2001:435-439.
[118] N.E.Huang,Z.Shen et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. 1998: 904-995.
[119]刘世双.基于局域波分析的几何信号处理技术研究.浙江工业大学硕士论文.2008:26-45.
[120] Saurabh Godha. Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. UCGE Reports. 2006:112-146.
[121] Klivansky. On long range dependence in NSFNET traffic. http://citeseer.ist.pus.edu/klivanskylongrange.html, 1994: 1- 6.
[122]秦永元,张洪钺,汪淑华.卡尔曼滤波与组合导航原理.陕西:西北工业大学出版社,2004:102-109.
[123]孙道省,邹志勤.组合导航系统中自适应滤波技术的研究.中国惯性技术学报.1999,7(2):25-29.
[124] C.Hu,W.Chen,Y.Chen and D.Liu. Adaptive Kalman filtering for vehicle navigation. Journal of Gloabal Postioning System. 2003, 2(1):42-47.
[125] Loebis D,Sutton R,Chudley J. Adaptive turning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Engineering Practice. 2004,12(12):1531-1539.
[126] W.D.Ding , J.L.Wang and C.Rizos. Improving adaptive Kalman estimation in GPS/INS integration. Journal of Navigation. 2007, 60:517-529.
[2] G.Garnes,S.Lee,A.Perez. INS/GPS space application for all flight dynamics boost through landing. PLANS , IEEE. 1996:457-463.
[3] K.Braden , C.Browning , H.Gelderloos. Integrated inertial navigation system/global positioning system for manned return vehicle autoland application. PLANS,IEEE. 1990:74-82.
[4]周露,闻新,周秀芬.组合导航制导系统的容错技术及特点.上海航天.1999,(2):58-61.
[5]董绪荣,张守信,华仲春.GPS/INS组合导航定位及其应用.长沙:国防科技大学出版社.1998:88-95.
[6]董渊文,官洪运,胡辉.GPS接收机伪码跟踪技术研究及仿真.系统仿真学报,2006,18(11):3209-3211.
[7]杨文辉.一种高动态GPS接收机跟踪环路设计.导航. 2005,41(1):45-52.
[8] M.G.Petovello , C.O.Driscoll and G.lachapelle. Weak signal carrier tracking using extended coherent integration with an ultra tight GNSS/IMU receiver. European Navigation Conference 2008. 2008:23-31.
[9] Kaplan. Understanding GPS: Principles and Applications.Boston: Artech House,2006:1-7.
[10]黄晓瑞.基于信息融合的车载组合导航系统研制.哈尔滨工业大学博士论文.2002:70-81.
[11] M.G.Petovello,D.Sun,G.Lachapelle,M.E.Cannon. Performance analysis of an ultra tightly integrated GPS and reduced IMU system. Conference of ION GNSS 2007. 2007:25-28.
[12] R.Babu and Jinling Wang. Dynamics performance of carrier and code tracking loops in ultra-tight GPS/INS/PL integration. ENC-GNSS 2005. 2005:19-26.
[13] D.Li,J.Wang,S.Babu. Enhancing the performance of ultra-tight integration of GPS/PL/INS:a federated filter approach. Symp.on GPS/GNSS. 2005: 1156-1165.
[14]程乃平,任宇飞,吕金飞.高动态扩频信号的载波跟踪技术研究.电子学报,2004,31(12):2147-2150.
[15]李小民.高动态环境GPS应用中的几个关键问题研究.北京航空航天大学博士论文,1999: 79-83.
[16]罗兴宇.基于DSP技术的高动态GPS接收机的研制.北京航空航天大学博士论文,2002: 56-72.
[17]薛文芳.高动态GPS接收机设计中几个关键问题的研究.北京航空航天大学博士论文,2002: 169-176.
[18]梁坤,施浒立.GPS接收机中码相位测量精化方法的研究.宇航学报,2007,28(3):571-575.
[19]吕艳梅,李小民,孙江生.高动态环境的GPS信号接收及其算法研究.电光与控制,2006,13(4):24-27.
[20]王伟,张廷新,史平彦.高动态环境下GPS信号跟踪算法综述.空间电子技术,2000,35(1):1-9.
[21]李小民,田庆民.适应高动态环境的GPS载波相位估计方法研究.武汉大学学报,2003,36(3):20-124.
[22] R.Babu,J.Wang. Real time data analysis of ultra tight GPS/INS integration. Symp.on GPS/GNSS(IGNSS2007). 2007:698-706.
[23] D.L,J.Wang. performance analysis of the ultra tight GPS/INS integration based on an improved Kalman filter design for tracking loops. Symp.on GPS/GNSS(IGNSS2006). 2006: 1123-1130.
[24] Wei-Lung Mao,Hen-Wai Tsao. A new fuzzy bandwidth carrier recovery system in GPS for robust phase tracking.IEEE Signal Processing Letters,2004,11(4):431-434.
[25]潘泉,于听,程咏梅,张洪才.信息融合理论的基本方法与进展.自动化学报.2003,29(4):599-615.
[26]张满生,张学庄,陈保平,许中华.新型GPS动态定位自适应卡尔曼滤波方法.中南工业大学学报,2003,34(5):543-546.
[27]柳海峰,姚郁,卢迪.Kalman滤波新息正交性抗野值法研究.电机与控制学报,2003,7(1):40-42.
[28]祝转民.Kalman滤波工程应用问题分析及改进方法研究.宇航学报.2002, 23(3):44-47.
[29] S.S.Blackman. Multiple sensor data association and fusion in aerospace application. Journal of Robot System , 1994 ,9(3):445-485.
[30] R.C.Luo.Dynamic Multi-Sensor Data Fusion System for Intelligent Robots.IEEE Journal of Robotocs and Automations. 1988, 15(4):386-396
[31] D.Whyte. Sensor models and Multisensor integration. International Journal of Robotics Research. 1988,(7):97-113.
[32] G.Kamberova. Decision-Theoretic approach to robust fusion of location Data. Journal of the Franklin Institute.1999,336(2): 269-284.
[33] L.J.Xu,Y.Z.Chen,P.Y.Cui. Improvement of D-S evidential theory in multisensor data fusion system. Fifth World Congress on Intelligent Control and Automation. 2004,4:3124-3128.
[34] H.Y.Yang,K.Park and J.G.Lee. A rotating sonar and a differential encoder data fusion for map-based dynamic positioning. Journal of Intelligent and Robotic Systems.2000,29:211-232.
[35] R.F.Gamble. Using software architecture to formally derive synchronous knowledge based systems. International Journal of Expert Systems. 1997,10(2):93-135.
[36]权太范.信息融合—神经网络-模糊推理理论与应用.北京:国防工业出版社,2002:224-275.
[37] L.Laurent,D.Renaud,K.Wojtek,S.Basel. Probabilistic and fuzzy information fusion applied to radar system ranking. Proceedings of SPIE-The International Society for Optical Engineering,2003:123-132.
[38] J.Leopoldo,S.Longhi,G.Venturini. Development and experimental validation of an adaptive extended Kalman filter for localization of mobile robots. IEEE Transactions on Robotics and Automation. 1999,15(2):219-229.
[39] T.D.Kinght. Rapid development of tightly integrated GPS/INS system. IEEE Aerospace and Electronic Systems Magazine,1997,12(4):14-19.
[40]蔡鹤皋,金明河,金峰.卡尔曼滤波与多传感器信息融合技术.模式识别与人工智能. 2003,13(3):248-253.
[41] J.Y.Liu,Y.R.Sun,X.F.He,etc. Design and realization on GPS/INS integrated navigation system. Transactions of Nanjing University of Aeronautics and Astronautics. 1998,15(2):165-171.
[42] S.Vathsal. Spacecraft attitude determination using a second order nonlinear filter. Journal of Guidance , Control , and Dynamics. 1987,10(6):559-566.
[43] M.Hassan , G.Salut , M.Singh. A decentralized computational algorithm for the global Kalman filter. IEEE Trans.Automatic Control. 1978,23:262-268.
[44] Y.M.Zhu et al. The optimality for the distributed Kalman filtering fusion with feedback. Automatica. 2001,37:1489-1493.
[45] H.Chen,T.Kirubarajan,Y.Bar-Shalom. Performance limits of Track to track fusion vs.centralized estimation. IEEE Trans.Aerospace and Electronic Systems,2003,39(3):386-398.
[46] Z.L.Deng,R.B.Qi. Multi-sensor information fusion suboptimal steady state Kalman filter. Chinese Science Abstracts,2000,6(2):183-184.
[47] N.A.Carlson. Federated square root filter for decentralized parallel processes. Proceeding of NAECON , Dayton ,1987:1448-1456.
[48]岳晓奎,袁建平.一种基于极大似然准则的自适应卡尔曼滤波算法.西北工业大学学报. 2005,23(4):469-474.
[49] A.P.Sage , G.W.Husa. Adaptive filtering with unknown prior statistics. Joint Automatic Control Conference. 1969:760-769.
[50]耿延睿,崔中兴,张洪钺,房建成.衰减因子自适应滤波及在组合导航中的应用.北京航空航天大学学报. 2004,30(5):434-437.
[51] C.Hide,T.Moore,M.Smith. Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. Symposium of Position Location and Navigation,2004,4:227-233.
[52] T.Yoshimura,T.Sueda. A Techinique for compensating the filter performance by a fictitious noise. Transactons on ASME,1978,100(2):154-156.
[53]尚捷,顾启泰. MIMS/GPS组合导航系统中卡尔曼滤波器设计与实验研究.中国惯性技术学报. 2005,13(2):45-48.
[54] J.Z.Sasiadek , Q.Wang , M.B.Zaremba. Fuzzy adaptive Kalman filtering for INS/GPS data fusion. Proceedings of the 15th IEEE International Symposium on Intelligent Control , Greece ,2000:181-186.
[55] C.Kyungho,A.Byungha,K.Hanheok. Intelligent adaptive gain adjustment and error compensation for improved tracking performance. IEICE Trans.on INF&SYST.2000,6(11): 1952-1959.
[56] R.Steven,Swanson. A fuzzy navigational state estimator for GPS/INS intergration. IEEE Symposium of Position Location and Navigation,1998: 541-548.
[57] Z.Ahmad,J.Zhang. Combination of multiple neural networks using data fusion techniques for enhanced nonlinear process Modelling. Computers and Chemical Engineering. 2005,2:295-308.
[58] A.Noureldin,R.Sharaf. INS/GPS data fusion technique utilizing radial basis functions neural networks. Position Location and Navigation Symposium. 2004,6:280-284.
[59] B.H.Yang,P.He,B.L.Wang. Research on fuzzy model and algorithms for data fusion. International Conference on Machine Learning and Cybernetics. 2003,4:2480-2484.
[60] L.Ojeda,J.Borenstein. Fuzzy logice estimation expert rule based position for mobile robots on rugged rerrain. Proceeding of the 2002 IEEE International Conference on Robotics and Automation. 2002:3170-322.
[61] S.J.Julier , J.K.Uhlmann , H.F.Durrant. A new approach for filtering nonlinear system. Proceedings of the American Control Conference,1995:1628-1632.
[62] S.J.Julier,J.K.Uhlmann,H.F.Durrant. A new method for nonlinear transformation of means and covariances in filters and estimator. IEEE Trans.Automatic Control,2000,45(3):477-482.
[63] E.Wan , V.Merwe. The unscented Kalman filter for nonlinear estimation. Proceedings of Symposium 2000 on Adaptive Systems for Signal,Communication and Control.2000:1255-1260.
[64] J.Simon,J.Jeffery,K.Uhlmann. Unscented filtering and nonlinear estimation. Proceeding of the IEEE, 2004,92(3):401-422.
[65] R.V.D.Merwe,E.A.Wan and S.Julier. Sigma point Kalman filters for nonlinear estimation and sensor fusion application to integrated navigation. AIAA Guidance,Navigation and Control Conference. 2004:425-437.
[66] J.L.Crassidis. Sigma point Kalman filtering for integrated GPS and inertial navigation. IEEE Transactions on Aerospace and Electronic Systems. 2005: 598-602.
[67] J.L.Crassidis and F.L.Markley. Unscented filtering for spacecraft attitude estimation. Journal of Guidance Control and Dynamics. 2003,45(5):910-927.
[68] A.Farina,B.Ristic and D.Benvenuti. Tracking a ballistic target: comparison of several nonlinear filters. IEEE Transactions on Aerospace and Electronic Systems. 2002,38(3):854-867.
[69] W.Li,H.Leng and Y.Zhou. Space time registration of radar and ESM using unscented Kalman filter. IEEE Transactions on Aerospace and Electronic Systems. 2004,40(3):824-836.
[70] B.Ristic,A.Farina,D.Benvenuti and M.S.Arulampalam. Performace bounds and comparison of nonlinear filters for tracking a ballistic object on re-rentry. IEEE Proceeding of Radar Sonor and Navigation. 2003,150(2):65-70.
[71] E.A.Wan and R.V.D.Merwe. The unscented Kalman filter. Kalman Filtering and Neural Networks. Haykin: Wiley Publishing. 2001,221-280.
[72] Y.C.Ho and R.C.K.Lee. A Bayesian approach to problems in stochastic estimation and control. IEEE Transactions on Automation Control. 1994,53(9):333-339.
[73] H.Carvalho,P.D.Moral. Optimal nonlinear filtering in GPS/INS integration. IEEE Transactions on AES,1997,33(3):835-849.
[74] P.J.Nordlund,F.Gustafsson. Sequential monte carlo filtering techniques applied to integrated navigation systems. Proceeding of the American Control Conference,2001:4375-4380.
[75] A.S.Babak. Approximate nonlinear filtering with applications tonavigation. Ph.D Thesis of Linkoping University. 2001:53-71.
[76] M.L.Psiaki,H.Jung. Extended Kalman filter methods for tracking weak GPS signals. Proceeding of ION GPS 2002, 2002, 2538-2553.
[77] D.M.Lin,J.B.Y.Tsui. A weak signal tracking technique for a stand-alone software GPS receiver. Proceeding of ION GPS 2002, 2002, 2534-2538.
[78] M.L.Psiaki. Smoother based GPS tracking in a software receiver. Proceeding of ION GPS 2001, 2001, 2900-2913.
[79] R.Babu, Jinling Wang and G.Rao. Analysis of ultra tight GPS/INS integrated system for navigation performance.IEEE 2008 International conference on signal processing, communication and networking, 2008, 234-237.
[80]张天光,王秀萍,王丽霞等译.捷联惯性导航技术.北京:国防工业出版社,2007:14-20.
[81]秦永元.惯性导航.北京:科学出版社,2006: 287-310.
[82]张士邈,刘放等.捷联惯导姿态算法若干问题研究.中国惯性技术学报. 2002, 10(2):1-7.
[83] M.Yu ,J.G.Lee and H.W.Park. Comparison of SDINS in flight aligment using equivalent error models. IEEE Tranasctions on Aerospace and Electronic Systems. 1999,35(3):1046-1052.
[84] Kaplan. Understanding GPS: Principles and Applications.Boston: Artech House,2006:14-46.
[85] J.Lee,B.Kim. A low noise fast lock phase locked loop with adaptive bandwidth control. IEEE Journal of Solid State Circuits. 2000,35(8):1137-1145.
[86] R.N.Crane. A simplified method for deep coupling of GPS and inertial data. Proceedings of ION NTM 2007. 2007:311-319.
[87]花俊,许斌,郝建华.高动态环境中伪码锁相环设计.装备指挥技术学院学报,2004,15(6):76-78.
[88]田明坤.高动态GPS接收机设计中几个关键问题的研究.北京航空航天大学博士论文,2002:124-137.
[89]薛鸿印.基于GPS芯片的高精度、高动态定位技术研究.哈尔滨工程大学博士论文,2001: 65-74.
[90] M.Hsieh, G. Sobelman. Clock and data recovery with adaptive loopgain for spread spectrum serdes applications. Proceedings of IEEE ISCAS 2005.2005:4883-4886.
[91] G.Egziabher. What is the difference between loose, tight, ultra-tight and deep integration strategies for INS and GNSS. GNSS Solutions. 2007, 2(1): 28-33.
[92] B.W. Parkinson, J.J. Spilker. GPS and inertial integration. American Institute of Aeronautics and Astronautics. 1996:187-218.
[93] O.S.Salychev , V.V.Voronov. Low cost INS/GPS integration: concepts and testing. Inttitute of Navigation National Technical Meeting,2000:26-28.
[94] J. D.Gautier,B. W. Parkinson and D. Gebre-Egziabher.Using the GPS/INSgeneralized evaluation tool (GIGET) for the comparison of loosely coupled, tightly coupled and ultra-tightly coupled integrated navigation systems. Proceedings of ION 59th Annual Meeting,2003: 23-25.
[95] W.D.Ding , J.L.Wang and C.Rizos. Improving adaptive Kalman estimation in GPS/INS integration. Journal of Navigation. 2007, 60(1):517-529.
[96] P.Daum, J. Beyer, T. K?hler. Aided inertial land navigation system with a minimum set of inertial sensors. Proceedings of IEEE-IEE Vehicle Navigation and Information Systems Conference.1994:284-291.
[97] S.Godha,M.G.Petovello and G.Lachapelle. Performance analysis of MEMS IMU/HSGPS/magnetic sensor integrated system in urban canyons. ION GNSS 2005,2005:13-16.
[98] Godha S. and M. E. Cannon.Integration of DGPS with a low cost MEMS based inertial measurement unit (IMU) for land vehicle navigation application. Proceedings of ION GNSS 2005 ,2005:333-345.
[99] W.Hamid, T.Abdelazim, N.E.Sheimy. Improvement of MEMS-IMU/GPS performance using fuzzy modeling. GPS Solutions , 2005 , 10:1046-1052.
[100] S.Nassar,X.Niu and N.E.Sheimy. Land vehicle INS/GPS accuratepositioning during GPS signal blockage periods. Journal of Surveying Engineering. 2005,26(12):365-372.
[101] X.Niu,S.Nassar and N.E.Sheimy. An accurate land vehicle MEMS IMU/GPS navigation system using 3D auxiliary velocity updates. Navigation. 2005,28(3):562-571.
[102] D.Li,J.Wang.Performacne analysis of the ultra-tight GPS/INS integrtation based on an improved Kalman filter design for tracking loops. International Global Navigation Satellite Systems Society. 2006:17-21.
[103] M.Petovello,G.Lachapelle. Comparison of vector-based software receiver implementations with application to ultra tight GPS/INS integration. Proceeding of GNSS 2006. 2006: 56-72.
[104] G.Egziabher, A. Razavi, P. Enge, J.D. Gautier, S. Pullen, B. Pervan, D. Akos. Sensitivity and performance analysis of doppler-aided GPS carrier tracking loops. Navigation, 2005,52(2): 49-60.
[105] Ravindra B, Wang J L, Gottapu R. Analysis of ultra-tight GPS/INS integrated system for navigation performance. IEEE 2008 International conference on signal processing. 2008:234-237.
[106] Pnay T R, Kaniuth B E. Deep Integration of navigation solution and signal processing. Proceeding of ION GNSS 2005. 2005:1095-1102.
[107] M.G.Petovello , C.O.Driscoll , G.Lachapelle. Carrier phase tracking of weak signals using different receiver architectures. Conference of ION NTM 2008. 2008:28-30.
[108] M.G.Petovello , G.Lachapelle. Comparison of vector based software receiver implementations with application to ultra tight GPS/SINS integration. Conference of ION GNSS 2006. 2006:26-29.
[109]陈军,潘高峰. GPS软件接收机基础.北京:电子工业出版社,2007:58-59.
[110] M.Wei and K.P.Schwarz. A strapdown inertial algorithm using and earth-fixed Cartesian frame. Navigation. 1990,2(37):153:167.
[111] Y.F.Jiang and P.Y.Lin. Error estimation of INS groud alignmentthrouth observability analysis. IEEE Transactions on Aerospace and Elictronic System. 1992:28(1),92-97.
[112] Y.F.Jiang and Y.P.Lin. Error estimation of INS ground alignment through observability analysis. IEEE Transactions on Aerospace and Electronic Systems. 1992,28(1):92-97.
[113]付梦印,邓志红,张继伟. Kalman滤波理论及其在导航系统中的应用.北京:科学出版社. 2003:8-32.
[114]邓自立.卡尔曼滤波与维纳滤波.哈尔滨工业大学出版社. 2001:102-160.
[115]姜军,杨亚非. SINS/GPS组合导航系统初始对准的可观测度分析.哈尔滨工业大学学报.2007,39(7):1025-1029.
[116] D.G.Meskin,I.Y.Bar-Itzhack. Observability analysis of piece wise constant systems with application to inertial navigation. Proceedings of th 29th conference on Dectation and Control. 1990:821-826.
[117]胡寿松等著.自动控制原理.第四版:科学出版社,2001:435-439.
[118] N.E.Huang,Z.Shen et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. 1998: 904-995.
[119]刘世双.基于局域波分析的几何信号处理技术研究.浙江工业大学硕士论文.2008:26-45.
[120] Saurabh Godha. Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. UCGE Reports. 2006:112-146.
[121] Klivansky. On long range dependence in NSFNET traffic. http://citeseer.ist.pus.edu/klivanskylongrange.html, 1994: 1- 6.
[122]秦永元,张洪钺,汪淑华.卡尔曼滤波与组合导航原理.陕西:西北工业大学出版社,2004:102-109.
[123]孙道省,邹志勤.组合导航系统中自适应滤波技术的研究.中国惯性技术学报.1999,7(2):25-29.
[124] C.Hu,W.Chen,Y.Chen and D.Liu. Adaptive Kalman filtering for vehicle navigation. Journal of Gloabal Postioning System. 2003, 2(1):42-47.
[125] Loebis D,Sutton R,Chudley J. Adaptive turning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Engineering Practice. 2004,12(12):1531-1539.
[126] W.D.Ding , J.L.Wang and C.Rizos. Improving adaptive Kalman estimation in GPS/INS integration. Journal of Navigation. 2007, 60:517-529.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |