[1] R. E. Kalman, and R. S. Bucy, New Results in Linear Filtering and Prediction, Trans. American Society of Mechanical Engineers, Series D, Journal of Basic Engineering, Vol. 83D, pp. 95–108, 1961.
[2] Branko Ristic, Sanjeev Arulampalam, and Neil Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, Boston, London, 2004.
[3] R. Siegwart and I. R. Nourbakhsh, Intoduction to Autonomous Mobile Robots, MIT Press, 2004.
[4] A. Howard, Multi- robot Simultaneous Localization and Mapping using Particle Filters, Robotics: Science and Systems I, pp. 201–208, 2005.
[5] N.J. Gordon, D.J. Salmond, and A.F.M. Smith, A Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation, IEE Proceedings F, 140(2):107–113, 1993.
[6] M. Isard, and A. Blake, Contour Tracking by Stochastic Propagation of Conditional Density, In Proc. of the European Conference of Computer Vision, 1996.
[7] D. Fox, S. Thrun, F. Dellaert, andW. Burgard, Particle Filters for Mobile Robot Localization, Sequential Monte Carlo Methods in Practice. Springer Verlag, New York, 2000.
[8] C. Hue, J. P. L. Cadre, and P. Perez, Sequential Monte Carlo Methods for Multiple Target Tracking and Data Fusion, IEEE Transactions on Signal Processing, Vol. 50, NO. 2, February 2002.
[9] B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House, 2004.
[10] K. Kanazawa, D. Koller, and S.J. Russell, Stochastic Simulation Algorithms for Dynamic Probabilistic Networks, In Proc. of the 11th Annual Conference on Uncertainty in AI (UAI), Montreal, Canada, 1995.
[11] F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. J. Nordlund, Particle Filters for Positioning, Navigation, and Tracking, IEEE Transactions on Signal Processing, Vol. 50, No. 2, February 2002.
[12] D. Schulz, W. Burgard, D. Fox, and A. B. Cremers, People Tracking with a Mobile Robot Using Sample-based Joint Probabilistic Data Association Filters, International Journal of Robotics Research (IJRR), 22(2), 2003.
[13] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, Vol. 50, No. 2, February 2002.
[14] N. Ikoma, N. Ichimura, T. Higuchi, and H. Maeda, Particle Filter Based Method for Maneuvering Target Tracking, IEEE International Workshop on Intelligent Signal Processing, Budapest, Hungary, May 24-25, pp.3– 8, 2001.
[15] R. R. Pitre, V. P. Jilkov, and X. R. Li, A comparative study of multiple model algorithms for maneuvering target tracking, Proc. 2005 SPIE Conf. Signal Processing, Sensor Fusion, and Target Recognition XIV, Orlando, FL, March 2005.
[16] T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe, Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association, IEEE Journal of Oceanic Engineering, Vol. 8, pp. 173–184, 1983.
[17] J. Vermaak, S. J. Godsill, and P. Perez, Monte Carlo Filtering for Multi-Target Tracking and Data Association, IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 309–332, January 2005.
[18] O. Frank, J. Nieto, J. Guivant, and S. Scheding, Multiple Target Tracking Using Sequential Monte Carlo Methods and Statistical Data Association, Proceedings of the 2003 IEEE/IRSJ, International Conference on Intelligent Robots and Systems, Las Vegas, Nevada, October 2003.
[19] Jao F. G. de Freitas, Bayesian Methods for Neural Networks, PhD thesis, Trinity College, University of Cambridge, 1999.
[20] P. D. Moral, A. Doucet, and A. Jasra, Sequential Monte Carlo Samplers, J. R. Statist. Soc. B, 68, Part 3, pp. 411–436, 2006.