组合导航系统中信息融合与故障检测理论研究
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摘要
现代高性能飞机对导航精度和可靠性的要求越来越高，随着机载非相似导航系统的增加，量测量也随之增加，这对改善卡尔曼滤波估计效果、提高组合导航系统精度无疑是有益的，但同时，故障率也随之增加。如果组合导航系统按照传统的设计方法设计成维数庞大的集中式卡尔曼滤波器，则计算量将以维数的三次方剧增，给实时计算带来困难；且容错能力较差，一旦某一子系统出现故障，所有正常工作的子系统都会受到污染。针对上述问题，本论文介绍了容错组合导航系统的联邦滤波理论，研究了飞机具有速率偏频激光捷联惯导、GPS、多普勒雷达等非相似导航系统配备条件下，容错组合导航系统的设计方案及性能。主要研究工作有：①研究了速率偏频激光捷联惯导的陀螺测量信号的解调算法；②设计了在速率偏频激光捷联惯导、GPS、多普勒雷达、大气数据系统、磁航向仪及垂直陀螺配备条件下联邦滤波组合导航系统，对联邦滤波器和集中滤波器的性能做了仿真对比分析；③研究了适用于联邦滤波器的系统级故障理论和方法；④探讨了利用速率偏频激光捷联惯导／挠性捷联惯导陀螺的混合测量信息，通过神经网络实现硬故障交互检测的方法。具有创新性的工作有：①推导了速率偏频激光捷联陀螺测量信息的解调算法；②应用神经网络解决了速率偏频激光捷联惯导／挠性捷联惯导陀螺间的硬故障交互检测。
The boom of modern naviation technology urgently requires its navigation system to be more accurate and more reliable. It is unpractical to meet both demands by any kind of navigation equipment separately. When the classical Centralized Kalman Filter (CKF) is used in this kind of integration, there will be two disadvantages: firstly, there exists real time computation difficulty. Secondly, the fault tolerance capability is poor. Once one of the subsystems breaks down, all the other subsystems are affected. In this thesis, on the basis of Federated Kalman filtering theory and aiming at a certain type of airplane equipped with many navigation equipments such as SINS, GPS,etc., the integrated navigation system with the fault tolerance property is designed. The following important work has been done: (1)Some special problems of rate-bias laser gyro were analysed. The problems include modulation and demodulation of the output and algorithm of the attitude updates; (2)The integrated navigation system with the fault tolerance property which equipped with many navigation equipments as SINS, GPS, DVS, ADS, MG, VG is designed,and compared with that of CKF via simulations; (3)The theories and methods of system level fault detection apply to Federated Filtering is investigated; (4)The problem about using the fused information of SINS based on rate-bias laser gyroscope/dynamical tune gyroscope to realize the component level hard fault interactions detection through a Back-Propagation neural network is discussed. The methodology and innovation in this paper is: (1)The problems which include modulation and demodulation of the rate biased laser gyro's output, algorithm of attitude updates,etc.are analysed. (2)The component level fault interactions detection of SINS based on rate-bias laser gyro/dynamical tune gyroscope by using artificial neural network is investigated.
The boom of modern naviation technology urgently requires its navigation system to be more accurate and more reliable. It is unpractical to meet both demands by any kind of navigation equipment separately. When the classical Centralized Kalman Filter (CKF) is used in this kind of integration, there will be two disadvantages: firstly, there exists real time computation difficulty. Secondly, the fault tolerance capability is poor. Once one of the subsystems breaks down, all the other subsystems are affected. In this thesis, on the basis of Federated Kalman filtering theory and aiming at a certain type of airplane equipped with many navigation equipments such as SINS, GPS,etc., the integrated navigation system with the fault tolerance property is designed. The following important work has been done: (1)Some special problems of rate-bias laser gyro were analysed. The problems include modulation and demodulation of the output and algorithm of the attitude updates; (2)The integrated navigation system with the fault tolerance property which equipped with many navigation equipments as SINS, GPS, DVS, ADS, MG, VG is designed,and compared with that of CKF via simulations; (3)The theories and methods of system level fault detection apply to Federated Filtering is investigated; (4)The problem about using the fused information of SINS based on rate-bias laser gyroscope/dynamical tune gyroscope to realize the component level hard fault interactions detection through a Back-Propagation neural network is discussed. The methodology and innovation in this paper is: (1)The problems which include modulation and demodulation of the rate biased laser gyro's output, algorithm of attitude updates,etc.are analysed. (2)The component level fault interactions detection of SINS based on rate-bias laser gyro/dynamical tune gyroscope by using artificial neural network is investigated.
引文
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[2] Kalman, R.E. and Bucy, R.S., "New Results in Linear Filtering and Prediction Theory", Journal of Basic Eng., March 1961, PP.95-108.
[3] Kalman, R.E., "New Methods in Wiener Filtering Theory", Proc. Symp. Eng. Appl. Random Functions Theory and Probability, John Wiley & Sons Inc., New York, 1963.
[4] Swerling, P., "First Order Error Propagation in a Stagewise Smoothing Procedure for Satellite observation", Journal of Astronautical Sciences, Vol.6, 1959, PP.46-52.
[5] Blum, M., "A Stagewise Parameter Estimation Procedure for Correlated data", Numer, Math., Vol.3, 1961. PP.202-208.
[6] Battin, R.H., "A Statistical Optimizing Navigation Procedure for Space Flight", ARS Journal, Vol.32, 1962, PP. 1681-1696.
[7] Bierman,G.J., "Sequential Square Rout Filtering and Smoothing of Discrete Linear Systems", Automatica, Vol. 10, March 1974, PP. 147-158.
[8] Carlson, N.A.," Fast triangular Factorization of the Square root filter", Journal AIAA Vol. 11, No.9, 1973, PP. 1259-1265.
[9] Kaminski, P.G., Bryson, A.E. and Schmidt, S.F., "Discrete. Square Root Filtering: A Survey of Current Techniques", IEEE Trans. Automatic Control, Vol. AC-16, No.6, Dec.1971, PP. 727-735.
[10] Andrews, A., "A square Root Formulation of the Kalman Covariance Equations", AIAA Journal, Vol.6, June 1968, PP. 1165-1166.
[11] Schmidt, S.F., "Computational Techniques in Kalman Filtering, in Theory and Applications of Kalman Filtering", NATO Advisory Coup for Aerospace Research and Development, AGARDograph B9, Feb. 1970.
[12] Dyer, P. and McReynolds, S., "Extension of Square Root Filtering to Include Process Noise", Journal Optimize. Theory Appl., Vol.3. No.6, 1969, PP. 444-459.
[13] Bierman, G.J., "Measurement Updating Using the U-D Factorization", proc. 1975 IEEE Conf. on Decision and Control, Hacston, Tex, PP.337-346.
[14] Thornton, C.L. and Bierman, G.J., Gram-Schmidt Bellantoni, J.F. and Dodge D.W., "A Square Root Formulation of the Kalman-Schmidt Filter", AIAA Journal, Vol.5, No.7, July 1967. PP. 1309-1314.
[15] Thornton, C.L. and Bierman, G.J., "UDU~T Covariance Factorization for Kalman Filtering", Algorithms for Covariance Propagation, proc. 1975 IEEE conf. on Decision and Control, Haceston, Tex. PP. 489-498.
[16] Sorenson, H.W. and Stubberud, A.R., "Nonlinear Filtering by Approximating of the Aposteriori Density", Int. J. Control, Vol. 18, 1968 PP.33-51.
[17] Phaneblf, R.J., "Approximate Nonlinear Estimation", Ph.D. Thesis, MIT, May 1968.
[18] Sunahara, Y., "An Approximate Method of State Estimation for Nonlinear Dynamical Systems", Joint Automatic Control Conference, Univ. of Colorndo, 1969.
[19] Wishner, R.P., Tabaczynski, J.A. and Athans, M., "A Comparison of Three Non-Linear
Filters", Automatic, Vol.5, 1969, PP. LS7-496.
[20] Leandes, C.T., Peller, J.B. and Stear, E.B., "Nonlinear Smoothing Theory", IEEE Trans. Systems Science and Cybernetics, Vol.SSC-6, No. 1, January 1970.
[21] Bucy. R.S. and Benne, K.D., "Disital Synthesis of Nonlinear Fillers", Automatics, Vol.7, No.3, May 1971. PP.287-289.
[22] De Figueiredo, R.J.P. and Jan, Y.G., "Spline Filters", proc. 2nd Symp. On Nonlinear Estimation Theory and its Applications, San Diego, 1971, PP. 127-141.
[23] Gelb, A. and Warren, e.s., "Direct Statistical Analysis of Nonlinear Systems, proc". AIAA Guibance and Control conf., Pab Alto, August, 1972.
[24] Bizuas, K.K. and Mahatanabis, A.K., "Suboptimal Algorithms for Nonlinear Smoothing", IEEE Trans. Aerospace and Electronic Systems, Vol. AES-9, No.4, July 1973, PP. 529-534.
[25] Weiss, H. and Moore, J.B., "Dither in Extended Kalman Filters", Optimization Days, Montreal, May 1977.
[26] Speyer, J.L., "Computation and Transmission Requirements for a Decentralized Linear-Quadratic-Gaussian Control Problem", IEEE Trans. Automatic Control, AC-24, No.2, Apr, 1979.
[27] Chang, T.S., "Comments on Computation and Transmission Requirements for a Decentralized Linear-Quadratic-Gaussion Control", IEEE Trans. Automatic Control, AC-zt, Vo.3, June 1980.
[28] Willsky, A.S., M.G. Bello, D.A.Castanon, B.C. Levy and G.C. "Verghese, Combining and Updating of Local Estimates and Regional maps Along Sets of One-Dimensional Tracks", IEEE Trans. on Automatic Control, AC-27.No.4, August 1982.
[29] Levy. B.C., D.A. Custnon, G.C. Verghese and A.S. Willsky, "A Seallering Franework for Decentralized Estimation Problems", Automatica, Vol. 49 No.4, April 1983.
[30] Castanon, D.A. and D.Teneketzis, "Distributed Estimation Algorithms for Nonlinear Systems", IEEE Trans. Automatic Control, AC-30, May 1985.
[31] Kerr, T.H., "Decentralized Filting and Redunbuncy Management for Multisensor Navigation", IEEE Trans. Aerospace and Electronic Systems, Vol.AES-23, No.1, January 1987, PP. 83-119.
[32] Bierman, G.J. and M.R. Belzer, "A Decentralized Square Root Information Filter/Smoother", Proc. of 24th IEEE Conf. on Decision and Control, Ft. Lauderdale, FL. Dec. 1985.
[33] M.Wei and K.P. Schwarz, "Testing a Decentralized Filter for GPS/INS Integration", Proc. of Position Location and Navigation Symposium, PLANS, 1990, PP.429-435.
[39] Carlson, N.A., "Federated Square Root Filter for Decentralized Parallel Proccesses", Proc. of National Aerospace and Elec. Conf., NAECON. Dayton, OH, May 1987.
[40] Carlson, N.A., "Federated Filter for Fault-Tolerant Integrated Navigation Systems", Proc. of Position Location and Navigation Symposium, PLANS, Orlando, FL, NOV. 1988.
[41] Loomis, P.V.W., N.A.Carlson and M.P. Berarduce:, "Common Kalman Filter: Fault-Tolerant Navigation for Next Generation Aircraft", Proc. of the Inst. of Navigation Conf., Santa Barbara, CA, January 1988, PP.38-45.
[42] Covino, J.M. and B.E. Griffiths, "A New Estimation Archrtecture for Multisensor Data Fusion", Proc. of the International Symposium and Exhibition on Optical Engineering and Photonics, Oxlando, FL, April 1991.
[43] Carlson, N.A., "Federated Filter for Fault-Tolerant Integrated Navigation", Chapter 8, AGARDograph. Aerospace Navigation Systems, 1993.
[44] Carlson, N.A. and M.P. Berarducci, "Federated Kalman Filter Simulation Results", Navigation Journal of the Institute of Navigation, Vol.41, No.3, Fall 1994, PP.297-321.
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