Development of State Estimation Filter Simulator Built on an Integrated GUI Framework
Masaru Morita and Takeshi Nishida
Kyushu Institute of Technology
1-1 Sensui-cho, Tobata-ku, Kitakyushu, Fukuoka 804-8550, Japan
-  A. Doucet and A. M. Johansen, “A tutorial on particle filtering and smoothing: Fifteen years later,” Handbook of Nonlinear Filtering, Vol.12, pp. 656-704, 2009.
-  A. Doucet, A. Smith, N. d. Freitas, and N. Gordon, “Sequential Monte Carlo Methods in Practice,” Information Science and Statistics, Springer New York, 2001.
-  S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics (Intelligent Robotics and Autonomous Agents),” The MIT Press, 2005.
-  S. Thrun, “Particle Filters in Robotics,” Proc. of the 17th Annual Conf. on Uncertainty in AI (UAI), 2002.
-  A. Yilmaz, O. Javed, and M. Shah, “Object Tracking: A Survey,” ACM Comput. Surv., Vol.38, No.4, December 2006.
-  O. Cappé, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential Monte Carlo,” Proc. of the IEEE, Vol.95, No.5, pp. 899-924, 2007.
-  H. Driessen and Y. Boers, “MAP estimation in particle filter tracking,” Proc. of the IETthe IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, pp. 41-45, IET, 2008.
-  S. Saha, Y. Boers, H. Driessen, P. K. Mandal, and A. Bagchi, “Particle based MAP state estimation: A comparison,” 12th Int. Conf. on Information Fusion 2009 (FUSION’09), pp. 278-283, 2009.
-  F. L. Lewis, L. Xie, and D. Popa, “Optimal and robust estimation: with an introduction to stochastic control theory,” Control engineering series, CRC Press, 2nd ed., 2008.
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