数据融合技术研究及其在船舶组合导航中的应用
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摘要
随着导航技术和控制理论的发展，船舶组合导航系统已经成为当今最主要的船舶导航系统。船舶组合导航系统包括许多导航传感器，因此如何充分有效的处理来自多传感器的数据已成为船舶组合导航系统的主要问题。而多传感器的数据融合技术为此提供了强有力的手段。本文以DR/GPS/劳兰C船舶组合导航系统为研究对象，从数据融合的角度出发，为提高船舶组合导航系统的导航精度和容错能力提供了有效的解决方法。
本论文分为两大部分。第一大部分是讨论怎样消除随机误差对船舶组合导航系统的干扰，主要建立了DR/GPS/劳兰C船舶组合导航系统的数学模型，并用集中式卡尔曼滤波算法和联邦滤波算法对其进行了仿真，验证了算法的有效性。
第二大部分是船舶组合导航系统的故障检测。在这部分里，首先介绍了故障检测的有关理论，接着进行了仿真。最后，对模糊神经网络理论在船舶组合导航系统故障检测中的应用进行了研究。
With the development of navigation technology and control theory, the integrated navigation system has already become the main navigation system at present. It is composed of many navigation sensors, so how to make the best of the data coming from the multi-sensors is the most important problem in the navigation system. The method of information fusion is one of the powerful means to solve this problem. In this paper, the DR/GPS/LoRan C integrated navigation system is studied in theory , and an effective way to solve this problem is given.
This paper consists of two parts. In the first part, it discusses how to avoid the random disturbance in the integrated navigation system. The mathematical model of this system is established and the system is simulated with the help of Kalman filter and federal filter. The results of the simulation show that both Kalman filter and federal filter are effective.
In the second part, fault diagnosis in the integrated navigation system is studied. At first, some theory of fault diagnosis is introduced. Then, some simulation is made. In the end, neural network theory is discussed and applied in the fault diagnosis.
本论文分为两大部分。第一大部分是讨论怎样消除随机误差对船舶组合导航系统的干扰，主要建立了DR/GPS/劳兰C船舶组合导航系统的数学模型，并用集中式卡尔曼滤波算法和联邦滤波算法对其进行了仿真，验证了算法的有效性。
第二大部分是船舶组合导航系统的故障检测。在这部分里，首先介绍了故障检测的有关理论，接着进行了仿真。最后，对模糊神经网络理论在船舶组合导航系统故障检测中的应用进行了研究。
With the development of navigation technology and control theory, the integrated navigation system has already become the main navigation system at present. It is composed of many navigation sensors, so how to make the best of the data coming from the multi-sensors is the most important problem in the navigation system. The method of information fusion is one of the powerful means to solve this problem. In this paper, the DR/GPS/LoRan C integrated navigation system is studied in theory , and an effective way to solve this problem is given.
This paper consists of two parts. In the first part, it discusses how to avoid the random disturbance in the integrated navigation system. The mathematical model of this system is established and the system is simulated with the help of Kalman filter and federal filter. The results of the simulation show that both Kalman filter and federal filter are effective.
In the second part, fault diagnosis in the integrated navigation system is studied. At first, some theory of fault diagnosis is introduced. Then, some simulation is made. In the end, neural network theory is discussed and applied in the fault diagnosis.
引文
[1]黄德鸣，程禄．惯性导航系统．国防工业出版社：12-13页
[2]孙枫，袁赣南，张晓红．组合导航系统．哈尔滨工程大学出版社．1996：264-267
[3]李洪志．信息融合技术．国防工业出版社．1-8页
[4]多传感器信息融合研究现状及展望．吴秋平，万德均，王庆．导航，1998．1．
[5]Luo R C, Michael G Kay. Multisensor integration and fusion in intelligent systems. IEEE Trans System, Man and Cybernetics, 1989, 19(5):901-931P
[6]Intelligence Fusion Pushed. Auiation Week and Space Technology, 1979, Jan. 29:205-211P
[7]Heeger D J, Hager G. Egomotion and the stabilized world. IEEE ICCV. 1988: 435-440P.
[8]Durrant-Whyte. Sensor models and multisensor integration. Int Journ of Robotics Reasearch. 1988,7(6):97-113.P
[9]王道平，张义忠．故障智能诊断系统的理论与方法．冶金工业出版社．2001．
[10]Waltz E. L., Linas J. Sensor fusion. Norwood, M A. Altec House, 1991.
[11]Jazwinski A. H. Stochastic Process and Filter Theory. Academic Press,1970:128-192P
[12]宋福香．差分GPS／惯性系统组合技术研究．哈尔滨工程大学硕士学位论文．1997：93-108页．
[13] Martin J Moorman, Thomas Bullock. A Federated Extended Kalman Filter with Application to Passive Target Tracking. IEEE PLANS. 1993.8.
[14]刘希斌．数据融合技术研究及其在组合导航中中的应用．哈尔滨工程大学硕士学位论文．2000．21-72页．
[15]Yong W. Li, Jianping Yuan and Hongxin Wu, "Decentralized Filter in the Estimation Space" International Federation of Automatic Control for Large Scale System 1998 conference (IFAC LSS'98), July 18-18,1998, Greece
[16] Azimi M,R, et.al. Fast Learning Process of Mulilayer NN using RLS Methods,IEEE,Trans. Signal Processing, 1992:40-48
[17] A.S.Willsky, MG.Bello, D.A,Castanon, B.C.Levy and G.C.Verghese. Combining and updating of local Estimation and Regional Maps Along Sets of One-dimentional Tracks. IEEE Trans. on AC, Vol.AC-27, No. 1, August.1982P
[18] T. Kerr, Decentralized Filtering and Redundancy Management for Multisensor Navigation. IEEE Trans. on AES, Vol. AES-23, No.1, Jan. 1985:191-209P
[19] H.R. Hashemipour, S.Roy and A.j.Laub, Decentralized structures for parallel Kalman filtering. IEEE Trans. on Ac, Vol.AC-33, No. 1, Jan, 1988.
[20] Neal A. Carlson and Michael P. Berarducci, Federated Kalman filter simulation results, Navigation, Vol. 41, No. 3, Fall 1994: 297-321P.
[21] Y. Gao, E. J. Krakiwsky, M. A. Abousalem and J. F. Melellan. Comparison and Analysis of Centralized, Decentralized, and Federated Filters. Navigation: Journal of the Institute of Navigation, Vol. 40. I, Spring 1995.P
[22] P. J. Lawrence, Jr, Capt and M.P. Berarducci,. Comparison of Federated and Centralized Kalman Filters with Fault Detection consideration. IEEE PLANS 94:128-134P.
[23] Neal A. Carlson, Federated square root filter for decentralized parallel processes. IEEE Transactions on Aerospace and Electronic Systems, AES-26 (1990): 517-525P
[24] 张崇猛，陈超英，庄良杰，刘飞．信息融合理论及其在INS／GPS／Doppler组合导航系统中的应用．中国惯性技术学报．1999，7(3)：1-8页．
[25] 秦永元，李岁劳．联邦滤波器公共参考信息的分配．导航．1998，1：61-65页．
[26] N.A.Carlson. Federated Filter for Fault-tolerant Integrated Navigation Systems. IEEE PLANS 88:110-119P.
[27] 黄德鸣，程禄．惯性导航系统．国防工业出版社：62-135页．
[28] 袁信，余济祥，陈哲．导航系统．航空工业出版社．1993：175-203，227-231页．
[29] 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．国防工业出版社．1998．1
[30]邱红专，张洪钺．联邦滤波器的精度分析与仿真．导航．1999，3：37-42页．
[31]Kailath T. An innovations approach to least-square estimation part I:linear filtering in adaptive white noise. IEEE Tans. on Automatic Control. 1968,13(5):645-655P.
[32]秦永元，张洪钺，汪叔华．卡尔曼滤波与组合导航原理．西北工业大学出版社，1998：85-90页．
[33]邱红专，张洪钺．联邦滤波器的容错性分析与仿真．导航．1999，9：27-35页．
[34]秦永元，牛惠芳．容错组合导航系统联邦滤波器设计中的信息同步．西北工业大学学报．1998．2：256-260页．
[35]俞济祥．卡尔曼滤波及其在惯性导航中的应用．航空专业教材编审室．1984：20-37，140-197页．
[36]敬喜．卡尔曼滤波器及其应用基础．国防工业出版社．1973：15-74，151-227页．
[37]李洪志．信息融合技术．国防工业出版社．1-8页
[38]Sun Q. and Alouani, A.T., Linear System State Estimation: a Neural Computing Approach. 1992 American Control Conference, Chicago, June, 1992.
[39]Jay K, and Mubarak Shan. Multi-Sensor Fusion; Aperspective. Proceeding of the 1990 IEEE International Conference on Robtics and Automation.
[40]马昕，袁信．组合导航系统状态／观测矢量融合算法比较．导航．1998．2：55-60页．
[41]Y. Bar-Shalom. The Effect of the Common Process Noise on the Two-sensor Fused-track Covariance. IEEE Transactions on Aerospace and Electronic Systems. Vol. AES-22, No. 6, Nov. 1986,803-805.
[42]陶俊勇，陶利民，杨定新．组合导航系统故障检测技术研究．国防科技大学学报．2001．1：31-35．
[43]孙枫，袁赣南，张晓红．组合导航系统．哈尔滨工程大学出版社．1996：22-24
[44]黄德鸣，张树侠，孙枫．平台罗经．国防工业出版社．1990，4：67-68
[2]孙枫，袁赣南，张晓红．组合导航系统．哈尔滨工程大学出版社．1996：264-267
[3]李洪志．信息融合技术．国防工业出版社．1-8页
[4]多传感器信息融合研究现状及展望．吴秋平，万德均，王庆．导航，1998．1．
[5]Luo R C, Michael G Kay. Multisensor integration and fusion in intelligent systems. IEEE Trans System, Man and Cybernetics, 1989, 19(5):901-931P
[6]Intelligence Fusion Pushed. Auiation Week and Space Technology, 1979, Jan. 29:205-211P
[7]Heeger D J, Hager G. Egomotion and the stabilized world. IEEE ICCV. 1988: 435-440P.
[8]Durrant-Whyte. Sensor models and multisensor integration. Int Journ of Robotics Reasearch. 1988,7(6):97-113.P
[9]王道平，张义忠．故障智能诊断系统的理论与方法．冶金工业出版社．2001．
[10]Waltz E. L., Linas J. Sensor fusion. Norwood, M A. Altec House, 1991.
[11]Jazwinski A. H. Stochastic Process and Filter Theory. Academic Press,1970:128-192P
[12]宋福香．差分GPS／惯性系统组合技术研究．哈尔滨工程大学硕士学位论文．1997：93-108页．
[13] Martin J Moorman, Thomas Bullock. A Federated Extended Kalman Filter with Application to Passive Target Tracking. IEEE PLANS. 1993.8.
[14]刘希斌．数据融合技术研究及其在组合导航中中的应用．哈尔滨工程大学硕士学位论文．2000．21-72页．
[15]Yong W. Li, Jianping Yuan and Hongxin Wu, "Decentralized Filter in the Estimation Space" International Federation of Automatic Control for Large Scale System 1998 conference (IFAC LSS'98), July 18-18,1998, Greece
[16] Azimi M,R, et.al. Fast Learning Process of Mulilayer NN using RLS Methods,IEEE,Trans. Signal Processing, 1992:40-48
[17] A.S.Willsky, MG.Bello, D.A,Castanon, B.C.Levy and G.C.Verghese. Combining and updating of local Estimation and Regional Maps Along Sets of One-dimentional Tracks. IEEE Trans. on AC, Vol.AC-27, No. 1, August.1982P
[18] T. Kerr, Decentralized Filtering and Redundancy Management for Multisensor Navigation. IEEE Trans. on AES, Vol. AES-23, No.1, Jan. 1985:191-209P
[19] H.R. Hashemipour, S.Roy and A.j.Laub, Decentralized structures for parallel Kalman filtering. IEEE Trans. on Ac, Vol.AC-33, No. 1, Jan, 1988.
[20] Neal A. Carlson and Michael P. Berarducci, Federated Kalman filter simulation results, Navigation, Vol. 41, No. 3, Fall 1994: 297-321P.
[21] Y. Gao, E. J. Krakiwsky, M. A. Abousalem and J. F. Melellan. Comparison and Analysis of Centralized, Decentralized, and Federated Filters. Navigation: Journal of the Institute of Navigation, Vol. 40. I, Spring 1995.P
[22] P. J. Lawrence, Jr, Capt and M.P. Berarducci,. Comparison of Federated and Centralized Kalman Filters with Fault Detection consideration. IEEE PLANS 94:128-134P.
[23] Neal A. Carlson, Federated square root filter for decentralized parallel processes. IEEE Transactions on Aerospace and Electronic Systems, AES-26 (1990): 517-525P
[24] 张崇猛，陈超英，庄良杰，刘飞．信息融合理论及其在INS／GPS／Doppler组合导航系统中的应用．中国惯性技术学报．1999，7(3)：1-8页．
[25] 秦永元，李岁劳．联邦滤波器公共参考信息的分配．导航．1998，1：61-65页．
[26] N.A.Carlson. Federated Filter for Fault-tolerant Integrated Navigation Systems. IEEE PLANS 88:110-119P.
[27] 黄德鸣，程禄．惯性导航系统．国防工业出版社：62-135页．
[28] 袁信，余济祥，陈哲．导航系统．航空工业出版社．1993：175-203，227-231页．
[29] 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．国防工业出版社．1998．1
[30]邱红专，张洪钺．联邦滤波器的精度分析与仿真．导航．1999，3：37-42页．
[31]Kailath T. An innovations approach to least-square estimation part I:linear filtering in adaptive white noise. IEEE Tans. on Automatic Control. 1968,13(5):645-655P.
[32]秦永元，张洪钺，汪叔华．卡尔曼滤波与组合导航原理．西北工业大学出版社，1998：85-90页．
[33]邱红专，张洪钺．联邦滤波器的容错性分析与仿真．导航．1999，9：27-35页．
[34]秦永元，牛惠芳．容错组合导航系统联邦滤波器设计中的信息同步．西北工业大学学报．1998．2：256-260页．
[35]俞济祥．卡尔曼滤波及其在惯性导航中的应用．航空专业教材编审室．1984：20-37，140-197页．
[36]敬喜．卡尔曼滤波器及其应用基础．国防工业出版社．1973：15-74，151-227页．
[37]李洪志．信息融合技术．国防工业出版社．1-8页
[38]Sun Q. and Alouani, A.T., Linear System State Estimation: a Neural Computing Approach. 1992 American Control Conference, Chicago, June, 1992.
[39]Jay K, and Mubarak Shan. Multi-Sensor Fusion; Aperspective. Proceeding of the 1990 IEEE International Conference on Robtics and Automation.
[40]马昕，袁信．组合导航系统状态／观测矢量融合算法比较．导航．1998．2：55-60页．
[41]Y. Bar-Shalom. The Effect of the Common Process Noise on the Two-sensor Fused-track Covariance. IEEE Transactions on Aerospace and Electronic Systems. Vol. AES-22, No. 6, Nov. 1986,803-805.
[42]陶俊勇，陶利民，杨定新．组合导航系统故障检测技术研究．国防科技大学学报．2001．1：31-35．
[43]孙枫，袁赣南，张晓红．组合导航系统．哈尔滨工程大学出版社．1996：22-24
[44]黄德鸣，张树侠，孙枫．平台罗经．国防工业出版社．1990，4：67-68