[1] G. D. Forney, “The Viterbi algorithm,” IEEE, vol. 61, pp. 268–278, 1973.
[2] L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal Decoding of Linear Codes for minimizing symbol error rate,” IEEE T Inform Theory, vol. 20, pp. 284–287,March 1974.
[3] R. Raheli, A. Polydoros, and C. Tzou, “Per-Survivor Processing: A General Approach to MLSE in Uncertain Environments,” IEEE T Commun., vol. 43, pp.354–364, 1975.
[4] S. Haykin, “Adaptive Filter Theory,” Prentice Hall, fifth edition, 2013.
[5] P. Diniz, “Adaptive Filtering: Algorithms and Practical Implementation,” Springer Science & Business Media, 2012.
[6] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Trans. ASME, Journal of Basic Engineering, vol. 82, pp. 35-45, 1960.
[7] Z. Zhu and H. Sadjadpour, “An adaptive per-survivor processing algorithm,” IEEE T Commun., vol. 50, pp. 1716-1718, November 2002.
[8] M. H. Majidi, M. Pourmir, and S. M. S. Sadough, “Kalman Filter-Based Discrete Data Estimation for Linear Dynamic Wireless Channels,” Proc. 3rd International Conference on Computer and Knowledge Engineering (ICCKE 2013), pp. 380-383, Oct. 31- Nov. 1 2013.
[9] Z. Ghahramani and G. E. Hinton, “Switching State-Space Models,” Tech. Rep., King’s College Road, Toronto M5S3H5, 1996.
[10] Z. Ghahramani and G. E. Hinton, “Variational Learning for Switching State-Space Models,” Neural Comput 12(4), pp. 831–864, 2000.
[11] C. A. Popescu and Y. S. Wong, “Nested Monte Carlo EM Algorithm for Switching State-Space Models,” IEEE T Knowl Data En, vol. 17, no. 12, Dec. 2005.
[12] H. Lu , D. Zeng and H. Chen, “Prospective Infectious Disease Outbreak Detection Using Markov Switching Models.” IEEE T Knowl Data En, vol. 22, no. 24, Dec. 2010.
[13] S. Saha and G. Hendeby, “Rao-Blackwellized particle filter for Markov modulated nonlinear dynamic systems, 2014 IEEE Workshop on Statistical Signal Processing, pp. 272-275, July 2014.
[14] J. Kalawoun, P. Pamphile, G. Celeux, K. Biletska, and M. Montaru, “Estimation of the battery state of charge: a switching Markov state-space model,” EUSIPCO'2015, Nice, France, Aug. 2015.
[15] A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. R. Statist. Soc., vol. 76, pp. 341-353, 1977.
[16] S. Julier and J. Uhlmann, “Unscented filtering and nonlinear estimation,” P IEEE, vol. 92, pp.401–422, Mar.2004.
[17] P. M. Djuric, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J. Miguez, “Particle filtering,” IEEE SIGNAL PROC MAG., vol. 20, pp. 19–38, Sept. 2003.
[18] Y. Li and X. Huang, “The simulation of independent Rayleigh faders,” IEEE T COMMUN, vol. 50, no. 9, pp. 1503-1514, 2002.
[19] H. Wang and P. Chang, “On verifying the first order Markovian assumption for a Rayleigh fading channel model,” IEEE T VEH TECHNOL, vol. 45, no. 2, pp. 353-357, May,1996.
[20] K. E. Baddour and N. C. Beaulieu, “Autoregressive models for fading channel simulation,” In Proceedings of the IEEE Global Telecommunications Conference, pp. 1187-1192, Nov. 2001.
[21] G. L. Stuber, Principles of Mobile Communications, Springer; 3rd edition, 2012.
[22] M. H. Majidi. “Bayesian estimation of discrete signals with local dependencies,” Ph.D. Thesis. Supélec, France, June 2014.
)
