GPS/DR车载组合定位系统数据融合算法研究
摘要
车辆定位技术是智能交通的关键技术之一。全球定位系统GPS(GlobalPosition System)具有良好的长期误差特性较差的短时误差特性,而航位推算DR(Dead Reckoning)系统却具有好的短期精度,差的长期精度。车载GPS/DR系统通过数据融合可提供高精度、高频率和可靠的定位数据。
论文在分析车载DR系统多传感器特性的基础上,导出了DR多传感器系统在当地水平坐标系下的力学编排公式,对来自电子指南针和角速率陀螺的角运动数据,加速度计和车辆里程表的线运动数据分别建立了分散卡尔曼滤波融合模型,进行车辆定位状态的最优估计,并对融合算法进行了仿真研究。
基于机动目标较合理的“当前统计”模型,根据车辆的动力学特性,提出了用模糊逻辑融合车辆状态数据,确定合理的加速度区间,使模型能更适合车辆运动的实际,定性分析和Matlab仿真表明加速度区间的模糊逻辑确定提高了定位精度和跟踪能力。此外,针对GPS/DR数据融合的卡尔曼滤波算法中可能出现的滤波发散,根据滤波收敛性判据论文还设计了基于模糊逻辑的卡尔曼滤波自适应发散抑制算法,并进行了仿真实验。
为提高对机动目标的跟踪性能,加速度在载体运动模型中必须予以考虑。论文提出了基于自适应神经模糊推理系统ANFIS(Adaptive Neural FuzzyInferential System)加速度估计自适应卡尔曼滤波算法,构建了ANFIS神经元网络结构,根据提取的特征数据,对载体机动进行融合估计。仿真研究表明,该算法可显著地提高定位精度和跟踪能力。
为进一步提高GPS/DR融合定位的连续性和精度,建立了增加速度观测,把加速度作为扰动输入的载体二维状态方程;提出了GPS/DR联合卡尔曼滤波数据融合算法以及基于神经元网络ANN(Artificial Neural Network)的GPS/DR数据融合算法模型以提高GPS/DR在GPS信号失锁时的连续定位能力,并进行了仿真验证。
论文从工程应用角度,系统的研究了GPS/DR车载组合定位的多传感器数据融合框架和算法,其成果为车载组合定位系统的物化实现奠定基础。
Vehicle location is the one of the important technology in intelligenttransportation system. Global position system named GPS is of superior long-termerror performance, but poor short-term accuracy, while dead reckoning systemnamed DR has good position precision in short-term but poor in long-term.GPS/DR integration provides position data with high precision, frequency andreliability.
On the base of analyzing the multi-sensor characteristic of vehicle DR system,the mechanics layout formulae in local horizontal reference frame were deduced.The decentralized kalman filters were founded, with which the angle movementdata offered by electro-compass and rate gyro and the line movement data suppliedby accelerometers and vehicle odometer were fused respectively, and the estimateoptimization of vehicle location states were obtained. The data fusion methodwere simulate.
For calculating the rational range of acceleration and making the vehicle modelto accord with the vehicle actual movement, basing on the current-statistics modelwhich is reasonable to maneuver target relatively and the kinetic characteristic ofvehicle, the fuzzy logic was brought forward to fuse the data of vehicle state. Theresult of qualitative analyzing and matlab simulations showed that the locationprecision and the tracking capability were improved by calculating theacceleration range through fuzzy logic. Furthermore, in order to solve thedivergence which is likely to emergence in kalman filter, the adaptive kalmanalgorithm based on fuzzy logic to avoid filter divergence was designed which relyon the convergence criterion of kalman filter, and simulation experimentationwere carried through.
In order to enhance the tracking capability of maneuver target, the accelerationin target movement model must be considered, thereby, the adaptive kalman filteralgorithm were put forward in which the acceleration were estimated by adaptiveneural fuzzy inferential system named ANFIS , and the structure of neural networkwas constructed. The state of target maneuver were fused by ANFIS according tothe character data distilled. The matlab simulations manifested that the locationprecision and the tracking capability were improved by the algorithm.
To improve the precision and continuity of GPS/DR more, the planar stateequation were established, in which the velocity state was added to as aobservation. In order to improve the location continuity when the GPS signal lost,the federal kalman filter fusion algorithm for GPS/DR and the GPS/DR fusionalgorithm model based on artificial neural network named ANN were put forward,and the simulation was performed.On the viewpoint of engineering application, the fusion framework and thealgorithm used for vehicle GPS/DR integration were studied in systemic, and theresults regard as the base of vehicle integration product.
论文在分析车载DR系统多传感器特性的基础上,导出了DR多传感器系统在当地水平坐标系下的力学编排公式,对来自电子指南针和角速率陀螺的角运动数据,加速度计和车辆里程表的线运动数据分别建立了分散卡尔曼滤波融合模型,进行车辆定位状态的最优估计,并对融合算法进行了仿真研究。
基于机动目标较合理的“当前统计”模型,根据车辆的动力学特性,提出了用模糊逻辑融合车辆状态数据,确定合理的加速度区间,使模型能更适合车辆运动的实际,定性分析和Matlab仿真表明加速度区间的模糊逻辑确定提高了定位精度和跟踪能力。此外,针对GPS/DR数据融合的卡尔曼滤波算法中可能出现的滤波发散,根据滤波收敛性判据论文还设计了基于模糊逻辑的卡尔曼滤波自适应发散抑制算法,并进行了仿真实验。
为提高对机动目标的跟踪性能,加速度在载体运动模型中必须予以考虑。论文提出了基于自适应神经模糊推理系统ANFIS(Adaptive Neural FuzzyInferential System)加速度估计自适应卡尔曼滤波算法,构建了ANFIS神经元网络结构,根据提取的特征数据,对载体机动进行融合估计。仿真研究表明,该算法可显著地提高定位精度和跟踪能力。
为进一步提高GPS/DR融合定位的连续性和精度,建立了增加速度观测,把加速度作为扰动输入的载体二维状态方程;提出了GPS/DR联合卡尔曼滤波数据融合算法以及基于神经元网络ANN(Artificial Neural Network)的GPS/DR数据融合算法模型以提高GPS/DR在GPS信号失锁时的连续定位能力,并进行了仿真验证。
论文从工程应用角度,系统的研究了GPS/DR车载组合定位的多传感器数据融合框架和算法,其成果为车载组合定位系统的物化实现奠定基础。
Vehicle location is the one of the important technology in intelligenttransportation system. Global position system named GPS is of superior long-termerror performance, but poor short-term accuracy, while dead reckoning systemnamed DR has good position precision in short-term but poor in long-term.GPS/DR integration provides position data with high precision, frequency andreliability.
On the base of analyzing the multi-sensor characteristic of vehicle DR system,the mechanics layout formulae in local horizontal reference frame were deduced.The decentralized kalman filters were founded, with which the angle movementdata offered by electro-compass and rate gyro and the line movement data suppliedby accelerometers and vehicle odometer were fused respectively, and the estimateoptimization of vehicle location states were obtained. The data fusion methodwere simulate.
For calculating the rational range of acceleration and making the vehicle modelto accord with the vehicle actual movement, basing on the current-statistics modelwhich is reasonable to maneuver target relatively and the kinetic characteristic ofvehicle, the fuzzy logic was brought forward to fuse the data of vehicle state. Theresult of qualitative analyzing and matlab simulations showed that the locationprecision and the tracking capability were improved by calculating theacceleration range through fuzzy logic. Furthermore, in order to solve thedivergence which is likely to emergence in kalman filter, the adaptive kalmanalgorithm based on fuzzy logic to avoid filter divergence was designed which relyon the convergence criterion of kalman filter, and simulation experimentationwere carried through.
In order to enhance the tracking capability of maneuver target, the accelerationin target movement model must be considered, thereby, the adaptive kalman filteralgorithm were put forward in which the acceleration were estimated by adaptiveneural fuzzy inferential system named ANFIS , and the structure of neural networkwas constructed. The state of target maneuver were fused by ANFIS according tothe character data distilled. The matlab simulations manifested that the locationprecision and the tracking capability were improved by the algorithm.
To improve the precision and continuity of GPS/DR more, the planar stateequation were established, in which the velocity state was added to as aobservation. In order to improve the location continuity when the GPS signal lost,the federal kalman filter fusion algorithm for GPS/DR and the GPS/DR fusionalgorithm model based on artificial neural network named ANN were put forward,and the simulation was performed.On the viewpoint of engineering application, the fusion framework and thealgorithm used for vehicle GPS/DR integration were studied in systemic, and theresults regard as the base of vehicle integration product.
引文
[1]张庶萍,郝春晖,城市交通发展策略取向,交通管理,2005.1,62~63
[2]张可,车辆导航系统关键技术研究,北京工业大学博士学位论文,2001.6
[3]毕军,车辆导航系统关键技术研究,北京理工大学博士学位论文,2003.6
[4]《中国智能运输系统体系框架》专题组,中国智能运输系统体系框架,北京:人民交通出版社,2003,17~28
[5]向怀坤,车辆导航系统关键技术研究,北京工业大学博士学位论文,2003.6
[6]J.Dillenburg, C.Lain, P.C.Nelson, Design of the advance traffic information center. Proceedings of 1995 Annual Meeting of ITS America,March, 1995,321~327
[7]M.L.G.Thoone, CARIN, A car information and navigation system. Philips Technical Review, 1987,43(11/12):317~329
[8]A.Ryan, F.Sommerville,Concertation and achievements report for the transport sector of the telematics applications programme, Proceedings of the 4th World Congress on Intelligent Transport Systems, Oct.1997,183~189
[9]Y.L.Zhao,Vehicle location and navigation systems. Norwood, MA: Artech House, 1995
[10]李金山,日本智能交通系统(ITS)研究综述,国外公路,2000, 20(4):33~35
[11]岳云,智能交通系统发展近况,科技与经济,1998,1:17~18
[12]吴小强,李鹏,曲卫民,智能交通系统研究回顾与展望,国外公路,2000,20(4):36~40
[13] T.Ito,Universal traffic management system in Japan,Proceedings of the 1th world Congress on Applications of Transport Telematics and Intelligent Vehicle-Highway Systems, 1995,34~39
[14]杨殿阁,车辆导航系统应用前景广阔, 汽车零部件也界, 2005.1:46~47
[15]彭飞,智能车辆定位导航系统的研究,北京航空航天大学博士论文,2000
[16]杨东凯,ITS系统分析与自动车辆定位,北京航空航天大学博士论文,2000
[17]王常荣,北京市公交系统汽车定位导航系统的设计,北京航空航天大学硕士论文,2000
[18]柏钢,面向ITS的车辆导航与定位技术的应用,东南大学博士论文,1999
[19]卢大伟,刘炳云,王庆,智能GPS/DR组合导航技术在TRACK一1型车载导航仪中的应用,导航,2000,第3期:108~112
[20]张小国,王庆,万德钧,车载组合导航系统中的分级地图匹配算法中国惯性技术学报,2000,8(3):46~51
[21]陈则王,袁信,面向ITS的车辆导航与定位技术,交通与计算机,2001, 19(6):23~25
[22]张三同,陆地车辆定位定向导航系统的研制与多传感器综合技术的研究,北京理工大学博士论文,1998
[23](日)社团法人 交通工学研究会.智能交通系统.北京:人民交通出版社. 2000.6,129~142
[24]黄卫,陈里得,智能运输系统(ITS)概论,北京:人民交通出版社,1999.9, 123~152
[25]王惠南,GPS 导航原理与应用,北京:科学出版社,2003,11~12
[26]刘基余,GPS 卫星导航定位原理与方法,北京:科学出版社,2003,42~54
[27]Chang-sun Yoo and Lee-ki Ahn, Low cost GPS/INS sensor fusion system for UAV navigation,IEEE,2003,8,A:1~8
[28]Edward J,Kraktwsky,Clyde B,Harrts, A Kalman filter for integrating dead reckoning, map matching and GPS positioning, IEEE 1998,39~46
[29]Kazuyuki Kobayashi,Kajiro Watanabe,Accurate navigation via sensor fusion of differential GPS and rate-gyro, IEEE,1994,556~560
[30]Wu Qiuping,Gao Zhongyu, An adaptive information fusion method to vehicle integrated navigation,IEEE,2002,248~254
[31] 郑 利 龙 , 曹 志 刚 , GPS 组 合 导 航 系 统 的 数 据 融 合 , 电 子 学报,2002,9:1384~1386
[32]常 青,郑平方等, 车载 GPS/DR 组合导航系统数据融合算法研究,通信学报,2000,2,Vol,21,No2:42~48
[33]高社生,李华星,INS/SAR 组合导航定位技术与应用,西安:西北工业大学出版社,2004,270~292
[34]戴亚平 刘征等译,多传感器数据融合理论及应用,北京:北京理工大学出版社,2004,2~50
[35]康耀红著, 数据融合理论与应用,西安:西安电子科技大学出版社,1997,1~20
[36]秦永元 张洪钺等,卡尔曼滤波与组合导航原理, 西安:西北工业大学出版社,1998,1~10
[37]申功勋,孙建峰,信息融合理论在惯性/天文/GPS组合导航系统中的应用,北京:国防工业出版社,1998,1~42
[38]万德钧,房建成,王庆,GPS 动态滤波的理论、方法及其应用,南京:江苏科技术出版社,2002,119~125
[39]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,1998,12~26
[40]Singer R A,Estimating Optimal tracking filter performance for manned maneuvering targets, IEEE Transactions on Aerospace and Electronic System, 1970,6(4):473~483
[41]Zhou Hongren,机动目标当前统计模型与自适应跟踪算法,航空学报,1983,4(1):73~86
[42]周宏仁,机动目标跟踪,北京:国防工业出版社,1991,65~92
[43]Y.T.Chan, A.G.C. Hu, and J, B, Plant, A Kalman filter based tracking scheme with input estimation, IEEE Trans, Aerosp, Electron, Syst., vol.AES-15, Mar.1979:237~244
[44]Y.Bar-Shalom and K.Birmiwal, Variable dimension filter for maneuvering target tracking, IEEE Trans, Aerosp, Electron, Syst, vol,AES-18, Sept, 1982,621~629
[45]Byeong Wan Ahn and Jae Weon Choi, A Modified Variable Dimension Filter with Input Estimation for Maneuvering Target Tracking, IEEE Proceedings of the American Control Conference Denver,Colorado, Jun.2003, 1266~1271
[46]F.Ibrahim,DGPS/INS integration using neural network methodology, Proceedings 12th IEEE International Conference on Tools with Artificial Intelligence, 2000,114~120
[47]L.Chin, Application of neural networks in target tracking data fusion, IEEE Trans, Aerosp, Electron, Syst., vol.30,Jan,1994: 281~287
[48] J.Z.Sasiadek and J.Khe, Sensor Fusion Based on Fuzzy Kalman Filter, 2th workshop on Robot Motion and Control,Oct.2001,275~283
[49]Antnio Tiano and Antnio Zirilli, Application of interval and fuzzy techniques to integrated navigation systems, IEEE,2001,13~18
[50]Steven R. and Swanson, A fuzzy navigational state estimator for GPS/INS integration,IEEE,1998,541~548
[51]P.Jorge and Escamilla-Ambrosio, Hybrid Kalman Filter-Fuzzy Logic Adaptive Multisensor Data Fusion Architectures, Proceedings of the 42nd IEEE Conference on Decision and Control,Maui,Hawaii USA,Dec, 2003, 5215~5220
[52] L.Chin, Application of neural networks in target tracking data fusion, IEEE Trans, Aerosp, Electron, Syst., vol. 30,Jan.1994, 281~287
[53] S.MeGinnity and G.W.Irwin,Fuzzy logic approach to maneuvering target tracking,Proc.Inst.Elect.Eng., vol.145, Dec.1998,337~341
[54] C.F.Juang and C.T.Lin, An on-line self-constructing neural fuzzy inference network and its applications, IEEE Trans. Fuzzy Syst.,vol.6, Feb.1998,12~32
[55]Fun-Bin Duh and Chin-Teng Lin, Tracking a Maneuvering Target Using Neural Fuzzy Network, IEEE Transaction on System,Man and Cybernetics-Part B: Cybernetics,Vol.34,No.1,Feb.2004:16~33
[56] Z.Jing, H.Xu, and X.Zhou, Information fusion and tracking of maneuvering targets with artificial neural network, in Proc, IEEE Int, Conf,Neural Networks (ICNN'94), 1994, 3403~3408
[57]Abdeinour G. Chand, On-line Detection & Correction of Kalman Filter Divergence by Fuzzy Logic,1993 American Control Conf, 1835~1839
[58]J,Z,Sasiadek and Q,Wang, Fuzzy adaptive Kalman filtering for INS/GPS data fusion, Proceedings of the 15th IEEE international Symposium on intelligent Control(ISIC 2000),Rio,patras,GREECE, Jul,2000,181~186
[59]J,Z,Sasiadek and Q,Wang, Sensor Fusion Based on Fuzzy Kalman Filtering for Autonomous Robot Vehicle, Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit,Michigan,May,1999, 2970~2975
[60]寇艳红,张其善,李先亮,车载GPS/DR组台导航系统的数据融台算法,北京航空航天大学学报,2003,Vol.29,No.3:264~268
[61]沈晓蓉 滕继涛 范跃祖, GPS/DR组合导航系统在车辆连续定位中的研究,压电与声光,2003,Vol 25,No.6:476~479
[62]Rogers, M.Robert, Land vehicle navigation filtering for a GPS/Dead-reckoning system. Proceedings of the National Technical Meeting of the Institute of Navigation,1997,703~708
[63] Wu Qiuping, Gao zhongyu, Wan Dejun, An adaptive information fusion method to vehicle integrated navigation. Proceedings of IEEE Position, Location and Navigation Symposium,2002,248~253
[64]房建成,申功勋,万德钧,GPS/DR组合导航系统自适应扩展卡尔曼滤波模型的建立,控制理论与应用,1998,15(3):385~390
[65]房建成,王庆,吴秋平,改进的车载DR系统自适应扩展卡尔曼滤波模型及仿真研究,东南大学学报,1999,29(1):35~39
[66]房建成,申功勋,万德钧,自适应卡尔曼滤波器在陆地车辆导航中的应用,北京航空航天大学学报,1999,25(2):235~239
[67]杨宜康,林勇,黄永宣,车辆自主导航系统的双滤波器模型及其导航算法,系统工程与电子技术,2002,24(6):39~41
[68]M.Hoshino, Y. Gunji, S.Oho. A Kalman filter to estimate direction for automotive navigation. Proceedings of the IEEE/SICE/RSJ International Conference Multisensor Fusion and Integration for Intelligent Systems,1996,145~150
[69]房建成,申功勋,万德钧,一种自适应联合卡尔曼滤波器及其在车载GPS/DR组合导航系统中的应用研究,中国惯性技术学报,1998, 6(4):1~6
[70]房建成,申功勋,万德钧,车载GPS/DR/地图匹配组合导航系统的自适应联合卡尔曼滤波模型,控制与决策,1999,14(5):448~452
[71]Y.Gao, E.J.Krakiwsky, M.A.Abousalem, Comparison and analysis of centralized, decentralized and federated filtering, Navigation, 1993,40(1):69~86
[72]Burak H,Kaygisiz and Aydan M,Erkmen, GPS/INS Enhancement Using Neural Networks for Autonomous Ground Vehicle Applications, Proceedings of the 2003 IEEE/RSJ intl, Conference on intelligent Robots and Systems, Las Vegas,Nevada,Oct,2003, 3763~3768
[73]以光衢,惯性导航原理,北京:航空工业出版社,1987,168~202
[74]H.A.Carlson, Federated Kalman filter for fault-tolerant integrated navigation systems, Proceedings of IEEE Position, Location and Navigation Symposium,1988,110~119
[75]H,A.Carlson, Federated Kalman filter simulation results, Navigation,Journal of the Institute of Navigation, 1994,41(3): 297~321
[76]Friedland B, Optimum steady-state position and velocity estimation using noisy sampled position data,IEEE Transactions on Aerospace and Electronic System,1973,9(6),906~111
[77]Hampton R L T,Cooke J R, Unsupervised Tracking of maneuvering Vehicles,IEEE Transactions on Aerospace and Electronic System,1973,9(2),197~207
[78]张树侠,孙 静,捷联式惯性导航系统,北京:国防工业出版社,1992,1~37
[79]董景新等,微惯性仪表—微机械加速度计,北京:清华大学出版社,2002,1~21
[80]韩尧松,GPS/DR车载组合导航系统硬件及压电陀螺信号处理,天津大学硕士学位论文,2005
[81]余志生,汽车理论,北京:机械工业出版社,2000,1~36
[82]黎苏 黎晓鹰 黎志勤,汽车发动机动态过程及其控制,北京:人民交通出版社,2001,86~95
[83]孙军,汽车发动机原理,合肥:安徽科学技术出版社,2001,101~110
[84]楼顺天 胡昌华等,基于 MATLAB 的系统分析与设计-模糊系统,西安:西安电子科技大学出版社,1~110
[85]张 文 修 梁 广 锡 等 ,模 糊 控 制 与 系 统 , 西 安 :西 安 交 通 大 学 出 版社,1998,1~27
[86] 王立新著,王迎军译, 模糊系统与模糊控制教程, 北京:清华大学出版社,2003,1~30
[87]王士同, 模糊系统、模糊神经网络及应用程序设计,上海:上海科学技术文献出版社,1997,1~58
[88]付梦印 邓志红等,Kalman 滤波理论及其在导航系统中的应用,北京:科学出版让,2003,45~102
[89]Cooper,S.and Durrant-Whyte H, A Kalman Filter Model for GPS Navigation of Land Vehcles,Proc.1994,IEEE Int. Conf. On Intelligent Robot and System,1994,157~163
[90]Kailath T, An innovations approach to least-square estimation part I: liner filtering in adaptive white noise,IEEE Transactions on Automatic Control, 1968,13(5),645~655
[91]T.C Wang and P.K.Varshney, A tracking algorithm for maneuvering targets, IEEE Trans,Aerosp,Electron,Syst.,vol.29,July 1993, 910~924
[92]Y.T.Chan, A.G.C. Hu, and J, B, Plant, A Kalman filter based tracking scheme with input estimation, IEEE Trans, Aerosp, Electron, Syst., vol.AES-15, Mar.1979, 237~244
[93]Y.Bar-Shalom and K.Birmiwal, Variable dimension filter for maneuvering target tracking, IEEE Trans, Aerosp, Electron, Syst, vol,AES-18, Sept, 1982,621~629
[94]N.H.Gholson and R.L.Moose, Maneuvering target tracking using adaptive state estimation, IEEE Trans, Aerosp, Electron, Syst., vol,AES-13, May 1977,310~317
[95]F.R.Castella, An adaptive two-dimensional Kalman tracking filter, IEEE Trans, Aerosp, Electron, Syst. Vol. AES-16, Nov.1980, 822~829
[96]C.B.Chang and J.A.Tabaczynski, Application of state estimation to target tracking, IEEE Trans, Automat, Contr, vol. AC-29, Feb.1984, 98~109
[97]R. K. Mehra, Approaches to adaptive filtering, IEEE Trans, Automat,Contr,, vol, AC-17, Oct, 1972, 693~698
[98]Y.N.Chung, D.L.Gustafson, and E. Emre, Extended solution to multiple maneuvering target tracking, IEEE Trans, Aerosp, Electron, Syst, vol, AES~26, Sept.1990,876~887
[99]高隽,人工神经网络原理及仿真实例,北京:机械工业出版社, 2003,电子工业出版社,2002,20~40
[100]阎平凡 张长水,人工神经网络与模拟进化计算,北京:清华大学出版社, 2002,37~49
[101]Foresee,F. D.,and M. T. Hagan,Gauss-Newton approximation to Bayesian regularization, Proceedings of the 1997 International Joint Conference on Neural Networks,1997,1930~1935
[102]MacKay, D. J. C., Bayesian interpolation, Neural Computation, vol. 4, no. 3, 1992,415~447
[103]Hagan, M. T., H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996
[104]Hagan,M. T.,and M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks,vol. 5,no. 6,1994,989~993
[2]张可,车辆导航系统关键技术研究,北京工业大学博士学位论文,2001.6
[3]毕军,车辆导航系统关键技术研究,北京理工大学博士学位论文,2003.6
[4]《中国智能运输系统体系框架》专题组,中国智能运输系统体系框架,北京:人民交通出版社,2003,17~28
[5]向怀坤,车辆导航系统关键技术研究,北京工业大学博士学位论文,2003.6
[6]J.Dillenburg, C.Lain, P.C.Nelson, Design of the advance traffic information center. Proceedings of 1995 Annual Meeting of ITS America,March, 1995,321~327
[7]M.L.G.Thoone, CARIN, A car information and navigation system. Philips Technical Review, 1987,43(11/12):317~329
[8]A.Ryan, F.Sommerville,Concertation and achievements report for the transport sector of the telematics applications programme, Proceedings of the 4th World Congress on Intelligent Transport Systems, Oct.1997,183~189
[9]Y.L.Zhao,Vehicle location and navigation systems. Norwood, MA: Artech House, 1995
[10]李金山,日本智能交通系统(ITS)研究综述,国外公路,2000, 20(4):33~35
[11]岳云,智能交通系统发展近况,科技与经济,1998,1:17~18
[12]吴小强,李鹏,曲卫民,智能交通系统研究回顾与展望,国外公路,2000,20(4):36~40
[13] T.Ito,Universal traffic management system in Japan,Proceedings of the 1th world Congress on Applications of Transport Telematics and Intelligent Vehicle-Highway Systems, 1995,34~39
[14]杨殿阁,车辆导航系统应用前景广阔, 汽车零部件也界, 2005.1:46~47
[15]彭飞,智能车辆定位导航系统的研究,北京航空航天大学博士论文,2000
[16]杨东凯,ITS系统分析与自动车辆定位,北京航空航天大学博士论文,2000
[17]王常荣,北京市公交系统汽车定位导航系统的设计,北京航空航天大学硕士论文,2000
[18]柏钢,面向ITS的车辆导航与定位技术的应用,东南大学博士论文,1999
[19]卢大伟,刘炳云,王庆,智能GPS/DR组合导航技术在TRACK一1型车载导航仪中的应用,导航,2000,第3期:108~112
[20]张小国,王庆,万德钧,车载组合导航系统中的分级地图匹配算法中国惯性技术学报,2000,8(3):46~51
[21]陈则王,袁信,面向ITS的车辆导航与定位技术,交通与计算机,2001, 19(6):23~25
[22]张三同,陆地车辆定位定向导航系统的研制与多传感器综合技术的研究,北京理工大学博士论文,1998
[23](日)社团法人 交通工学研究会.智能交通系统.北京:人民交通出版社. 2000.6,129~142
[24]黄卫,陈里得,智能运输系统(ITS)概论,北京:人民交通出版社,1999.9, 123~152
[25]王惠南,GPS 导航原理与应用,北京:科学出版社,2003,11~12
[26]刘基余,GPS 卫星导航定位原理与方法,北京:科学出版社,2003,42~54
[27]Chang-sun Yoo and Lee-ki Ahn, Low cost GPS/INS sensor fusion system for UAV navigation,IEEE,2003,8,A:1~8
[28]Edward J,Kraktwsky,Clyde B,Harrts, A Kalman filter for integrating dead reckoning, map matching and GPS positioning, IEEE 1998,39~46
[29]Kazuyuki Kobayashi,Kajiro Watanabe,Accurate navigation via sensor fusion of differential GPS and rate-gyro, IEEE,1994,556~560
[30]Wu Qiuping,Gao Zhongyu, An adaptive information fusion method to vehicle integrated navigation,IEEE,2002,248~254
[31] 郑 利 龙 , 曹 志 刚 , GPS 组 合 导 航 系 统 的 数 据 融 合 , 电 子 学报,2002,9:1384~1386
[32]常 青,郑平方等, 车载 GPS/DR 组合导航系统数据融合算法研究,通信学报,2000,2,Vol,21,No2:42~48
[33]高社生,李华星,INS/SAR 组合导航定位技术与应用,西安:西北工业大学出版社,2004,270~292
[34]戴亚平 刘征等译,多传感器数据融合理论及应用,北京:北京理工大学出版社,2004,2~50
[35]康耀红著, 数据融合理论与应用,西安:西安电子科技大学出版社,1997,1~20
[36]秦永元 张洪钺等,卡尔曼滤波与组合导航原理, 西安:西北工业大学出版社,1998,1~10
[37]申功勋,孙建峰,信息融合理论在惯性/天文/GPS组合导航系统中的应用,北京:国防工业出版社,1998,1~42
[38]万德钧,房建成,王庆,GPS 动态滤波的理论、方法及其应用,南京:江苏科技术出版社,2002,119~125
[39]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,1998,12~26
[40]Singer R A,Estimating Optimal tracking filter performance for manned maneuvering targets, IEEE Transactions on Aerospace and Electronic System, 1970,6(4):473~483
[41]Zhou Hongren,机动目标当前统计模型与自适应跟踪算法,航空学报,1983,4(1):73~86
[42]周宏仁,机动目标跟踪,北京:国防工业出版社,1991,65~92
[43]Y.T.Chan, A.G.C. Hu, and J, B, Plant, A Kalman filter based tracking scheme with input estimation, IEEE Trans, Aerosp, Electron, Syst., vol.AES-15, Mar.1979:237~244
[44]Y.Bar-Shalom and K.Birmiwal, Variable dimension filter for maneuvering target tracking, IEEE Trans, Aerosp, Electron, Syst, vol,AES-18, Sept, 1982,621~629
[45]Byeong Wan Ahn and Jae Weon Choi, A Modified Variable Dimension Filter with Input Estimation for Maneuvering Target Tracking, IEEE Proceedings of the American Control Conference Denver,Colorado, Jun.2003, 1266~1271
[46]F.Ibrahim,DGPS/INS integration using neural network methodology, Proceedings 12th IEEE International Conference on Tools with Artificial Intelligence, 2000,114~120
[47]L.Chin, Application of neural networks in target tracking data fusion, IEEE Trans, Aerosp, Electron, Syst., vol.30,Jan,1994: 281~287
[48] J.Z.Sasiadek and J.Khe, Sensor Fusion Based on Fuzzy Kalman Filter, 2th workshop on Robot Motion and Control,Oct.2001,275~283
[49]Antnio Tiano and Antnio Zirilli, Application of interval and fuzzy techniques to integrated navigation systems, IEEE,2001,13~18
[50]Steven R. and Swanson, A fuzzy navigational state estimator for GPS/INS integration,IEEE,1998,541~548
[51]P.Jorge and Escamilla-Ambrosio, Hybrid Kalman Filter-Fuzzy Logic Adaptive Multisensor Data Fusion Architectures, Proceedings of the 42nd IEEE Conference on Decision and Control,Maui,Hawaii USA,Dec, 2003, 5215~5220
[52] L.Chin, Application of neural networks in target tracking data fusion, IEEE Trans, Aerosp, Electron, Syst., vol. 30,Jan.1994, 281~287
[53] S.MeGinnity and G.W.Irwin,Fuzzy logic approach to maneuvering target tracking,Proc.Inst.Elect.Eng., vol.145, Dec.1998,337~341
[54] C.F.Juang and C.T.Lin, An on-line self-constructing neural fuzzy inference network and its applications, IEEE Trans. Fuzzy Syst.,vol.6, Feb.1998,12~32
[55]Fun-Bin Duh and Chin-Teng Lin, Tracking a Maneuvering Target Using Neural Fuzzy Network, IEEE Transaction on System,Man and Cybernetics-Part B: Cybernetics,Vol.34,No.1,Feb.2004:16~33
[56] Z.Jing, H.Xu, and X.Zhou, Information fusion and tracking of maneuvering targets with artificial neural network, in Proc, IEEE Int, Conf,Neural Networks (ICNN'94), 1994, 3403~3408
[57]Abdeinour G. Chand, On-line Detection & Correction of Kalman Filter Divergence by Fuzzy Logic,1993 American Control Conf, 1835~1839
[58]J,Z,Sasiadek and Q,Wang, Fuzzy adaptive Kalman filtering for INS/GPS data fusion, Proceedings of the 15th IEEE international Symposium on intelligent Control(ISIC 2000),Rio,patras,GREECE, Jul,2000,181~186
[59]J,Z,Sasiadek and Q,Wang, Sensor Fusion Based on Fuzzy Kalman Filtering for Autonomous Robot Vehicle, Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit,Michigan,May,1999, 2970~2975
[60]寇艳红,张其善,李先亮,车载GPS/DR组台导航系统的数据融台算法,北京航空航天大学学报,2003,Vol.29,No.3:264~268
[61]沈晓蓉 滕继涛 范跃祖, GPS/DR组合导航系统在车辆连续定位中的研究,压电与声光,2003,Vol 25,No.6:476~479
[62]Rogers, M.Robert, Land vehicle navigation filtering for a GPS/Dead-reckoning system. Proceedings of the National Technical Meeting of the Institute of Navigation,1997,703~708
[63] Wu Qiuping, Gao zhongyu, Wan Dejun, An adaptive information fusion method to vehicle integrated navigation. Proceedings of IEEE Position, Location and Navigation Symposium,2002,248~253
[64]房建成,申功勋,万德钧,GPS/DR组合导航系统自适应扩展卡尔曼滤波模型的建立,控制理论与应用,1998,15(3):385~390
[65]房建成,王庆,吴秋平,改进的车载DR系统自适应扩展卡尔曼滤波模型及仿真研究,东南大学学报,1999,29(1):35~39
[66]房建成,申功勋,万德钧,自适应卡尔曼滤波器在陆地车辆导航中的应用,北京航空航天大学学报,1999,25(2):235~239
[67]杨宜康,林勇,黄永宣,车辆自主导航系统的双滤波器模型及其导航算法,系统工程与电子技术,2002,24(6):39~41
[68]M.Hoshino, Y. Gunji, S.Oho. A Kalman filter to estimate direction for automotive navigation. Proceedings of the IEEE/SICE/RSJ International Conference Multisensor Fusion and Integration for Intelligent Systems,1996,145~150
[69]房建成,申功勋,万德钧,一种自适应联合卡尔曼滤波器及其在车载GPS/DR组合导航系统中的应用研究,中国惯性技术学报,1998, 6(4):1~6
[70]房建成,申功勋,万德钧,车载GPS/DR/地图匹配组合导航系统的自适应联合卡尔曼滤波模型,控制与决策,1999,14(5):448~452
[71]Y.Gao, E.J.Krakiwsky, M.A.Abousalem, Comparison and analysis of centralized, decentralized and federated filtering, Navigation, 1993,40(1):69~86
[72]Burak H,Kaygisiz and Aydan M,Erkmen, GPS/INS Enhancement Using Neural Networks for Autonomous Ground Vehicle Applications, Proceedings of the 2003 IEEE/RSJ intl, Conference on intelligent Robots and Systems, Las Vegas,Nevada,Oct,2003, 3763~3768
[73]以光衢,惯性导航原理,北京:航空工业出版社,1987,168~202
[74]H.A.Carlson, Federated Kalman filter for fault-tolerant integrated navigation systems, Proceedings of IEEE Position, Location and Navigation Symposium,1988,110~119
[75]H,A.Carlson, Federated Kalman filter simulation results, Navigation,Journal of the Institute of Navigation, 1994,41(3): 297~321
[76]Friedland B, Optimum steady-state position and velocity estimation using noisy sampled position data,IEEE Transactions on Aerospace and Electronic System,1973,9(6),906~111
[77]Hampton R L T,Cooke J R, Unsupervised Tracking of maneuvering Vehicles,IEEE Transactions on Aerospace and Electronic System,1973,9(2),197~207
[78]张树侠,孙 静,捷联式惯性导航系统,北京:国防工业出版社,1992,1~37
[79]董景新等,微惯性仪表—微机械加速度计,北京:清华大学出版社,2002,1~21
[80]韩尧松,GPS/DR车载组合导航系统硬件及压电陀螺信号处理,天津大学硕士学位论文,2005
[81]余志生,汽车理论,北京:机械工业出版社,2000,1~36
[82]黎苏 黎晓鹰 黎志勤,汽车发动机动态过程及其控制,北京:人民交通出版社,2001,86~95
[83]孙军,汽车发动机原理,合肥:安徽科学技术出版社,2001,101~110
[84]楼顺天 胡昌华等,基于 MATLAB 的系统分析与设计-模糊系统,西安:西安电子科技大学出版社,1~110
[85]张 文 修 梁 广 锡 等 ,模 糊 控 制 与 系 统 , 西 安 :西 安 交 通 大 学 出 版社,1998,1~27
[86] 王立新著,王迎军译, 模糊系统与模糊控制教程, 北京:清华大学出版社,2003,1~30
[87]王士同, 模糊系统、模糊神经网络及应用程序设计,上海:上海科学技术文献出版社,1997,1~58
[88]付梦印 邓志红等,Kalman 滤波理论及其在导航系统中的应用,北京:科学出版让,2003,45~102
[89]Cooper,S.and Durrant-Whyte H, A Kalman Filter Model for GPS Navigation of Land Vehcles,Proc.1994,IEEE Int. Conf. On Intelligent Robot and System,1994,157~163
[90]Kailath T, An innovations approach to least-square estimation part I: liner filtering in adaptive white noise,IEEE Transactions on Automatic Control, 1968,13(5),645~655
[91]T.C Wang and P.K.Varshney, A tracking algorithm for maneuvering targets, IEEE Trans,Aerosp,Electron,Syst.,vol.29,July 1993, 910~924
[92]Y.T.Chan, A.G.C. Hu, and J, B, Plant, A Kalman filter based tracking scheme with input estimation, IEEE Trans, Aerosp, Electron, Syst., vol.AES-15, Mar.1979, 237~244
[93]Y.Bar-Shalom and K.Birmiwal, Variable dimension filter for maneuvering target tracking, IEEE Trans, Aerosp, Electron, Syst, vol,AES-18, Sept, 1982,621~629
[94]N.H.Gholson and R.L.Moose, Maneuvering target tracking using adaptive state estimation, IEEE Trans, Aerosp, Electron, Syst., vol,AES-13, May 1977,310~317
[95]F.R.Castella, An adaptive two-dimensional Kalman tracking filter, IEEE Trans, Aerosp, Electron, Syst. Vol. AES-16, Nov.1980, 822~829
[96]C.B.Chang and J.A.Tabaczynski, Application of state estimation to target tracking, IEEE Trans, Automat, Contr, vol. AC-29, Feb.1984, 98~109
[97]R. K. Mehra, Approaches to adaptive filtering, IEEE Trans, Automat,Contr,, vol, AC-17, Oct, 1972, 693~698
[98]Y.N.Chung, D.L.Gustafson, and E. Emre, Extended solution to multiple maneuvering target tracking, IEEE Trans, Aerosp, Electron, Syst, vol, AES~26, Sept.1990,876~887
[99]高隽,人工神经网络原理及仿真实例,北京:机械工业出版社, 2003,电子工业出版社,2002,20~40
[100]阎平凡 张长水,人工神经网络与模拟进化计算,北京:清华大学出版社, 2002,37~49
[101]Foresee,F. D.,and M. T. Hagan,Gauss-Newton approximation to Bayesian regularization, Proceedings of the 1997 International Joint Conference on Neural Networks,1997,1930~1935
[102]MacKay, D. J. C., Bayesian interpolation, Neural Computation, vol. 4, no. 3, 1992,415~447
[103]Hagan, M. T., H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996
[104]Hagan,M. T.,and M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks,vol. 5,no. 6,1994,989~993