Prospects of INS/CNS/GNSS Integrated Navigation Technology

With the rapid development of space science and growing interest in space exploration missions, the performance requirement of navigation systems for space vehicles becomes demanding; therefore, a single means of navigation has been unable to meet the needs of engineering applications. The integrated technology of inertial navigation system (INS), celestial navigation system (CNS), and global navigation satellite system (GNSS) has become an important development direction of navigation technology due to its high precision, high real-timeliness, high reliability, as well as integrated and intelligent capabilities.

[1]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.

[2]  P. Reiser,et al.  Genetic algorithms in engineering systems: innovations and applications , 1996 .

[3]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[4]  Churn-Jung Lian,et al.  A conservative approach to distributed belief fusion , 2000, Proceedings of the Third International Conference on Information Fusion.

[5]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[6]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[7]  Wang Guangyuan,et al.  On fuzzy random linear programming , 1994 .

[8]  Brian C. Williams,et al.  Hybrid estimation of complex systems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Shen Qun-tai,et al.  Evolutionary design of RBF neural network based on multi-species cooperative particle swarm optimizer , 2006 .

[10]  Xiaoji Niu,et al.  Analysis and Modeling of Inertial Sensors Using Allan Variance , 2008, IEEE Transactions on Instrumentation and Measurement.

[11]  Zhou Feng-qi.  Reconfigurable fault tolerant space manipulator design , 2004 .

[12]  Han Chon.  MULTI-SENSOR INFORMATION FUSION AND AUTOMATION , 2002 .

[13]  Lei Guo,et al.  Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises , 2005, Proceedings of the 2005, American Control Conference, 2005..

[14]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[15]  Zheng Pei,et al.  Dynamic adaptive fuzzy neural-network identification and its application , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[16]  Frank H. Bauer,et al.  The Inertial Stellar Compass (ISC): A Multifunction, Low Power, Attitude Determination Technology Breakthrough , 2003 .

[17]  Dieter Fox,et al.  Real-time particle filters , 2004, Proceedings of the IEEE.

[18]  D. Petri,et al.  Tutorial 14: multisensor data fusion , 2008, IEEE Instrumentation & Measurement Magazine.

[19]  Didier Dubois,et al.  Possibility theory and its applications: a retrospective and prospective view , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[20]  Cui Ping-yuan.  State Estimation of Integrated Navigation System Based on Neural Network , 2004 .

[21]  Inseok Hwang,et al.  Performance analysis of hybrid estimation algorithms , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[22]  Lei Guo,et al.  PID controller design for output PDFs of stochastic systems using linear matrix inequalities , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Hong Qiao,et al.  A reference model approach to stability analysis of neural networks , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[24]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[25]  Jeffrey K. Uhlmann,et al.  Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[26]  A. Farina,et al.  Tracking a ballistic target: comparison of several nonlinear filters , 2002 .

[27]  S. Mori,et al.  Random sets in data fusion: formalism to new algorithms , 2000, Proceedings of the Third International Conference on Information Fusion.

[28]  Robert T. M'Closkey,et al.  A systematic method for tuning the dynamics of electrostatically actuated vibratory gyros , 2006, IEEE Transactions on Control Systems Technology.

[29]  Zhang Shang-yue.  Optimal celestial positioning constellation combination based on genetic algorithm , 2004 .

[30]  Yasuo Morooka,et al.  Fuzzy and Neural Hybrid Expert Systems: Synergetic AI , 1995, IEEE Expert.

[31]  Tye Brady,et al.  The Inertial Stellar Compass: A New Direction in Spacecraft Attitude Determination , 2002 .

[32]  Hong Wang,et al.  Bounded Dynamic Stochastic Systems , 2012 .

[33]  Mauro Birattari,et al.  On the Invariance of Ant Colony Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[34]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[35]  Shi Wen.  Research on application of random sets theory to data fusion , 2002 .

[36]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary and genetic algorithms: theory and applications , 1997 .

[37]  Volume Assp,et al.  ACOUSTICS. SPEECH. AND SIGNAL PROCESSING , 1983 .

[38]  Saint Petersburg,et al.  Second International Conference , 2001 .

[39]  I. Baruch,et al.  A multi-model parameter and state estimation of mechanical systems , 2000, ISIE'2000. Proceedings of the 2000 IEEE International Symposium on Industrial Electronics (Cat. No.00TH8543).

[40]  Darryl Stewart,et al.  Improving speech recognition performance by using multi-model approaches , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).