Recursive Kalman-type optimal estimation and detection of hidden Markov chains

Signal Processing

Volumn 51, Issue 1, 1996, Pages 55-64

  Baccarelli, Enzo ^a   Cusani, Roberto ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; ERROR ANALYSIS; ESTIMATION; KALMAN FILTERING; MARKOV PROCESSES; PERFORMANCE; PROBABILITY; RECURSIVE FUNCTIONS; SIGNAL DETECTION; STATE ESTIMATION;

A POSTERIORI PROBABILITIES; MINIMUM MEAN SQUARE ERROR; NONLINEAR FILTERS; SMOOTHERS; STATE SEQUENCE;

SIGNAL PROCESSING;

EID: 0030141619     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/0165-1684(96)00030-8     Document Type: Article

References (16)

1. E. Baccarelli and R. Cusani, Universal optimal estimation and detection of Markov chains over noisy discrete channels, INFOCOM Dept Tech. Rep. No. 005-02-94, Univ. of Rome "La Sapienza", Rome, Italy, February 1994.
2. E. Baccarelli, R. Cusani and G. Di Blasio, "A new family of decision delay-constrained MAP decoders for data transmission over noisy channels with ISI and soft-decision demodulation", Proc. IEEE Internat. Symp. on Inform. Theory, Whistler, Canada, 1995, p. 333.
3. E. Baccarelli, R. Cusani and L. Piazzo, "A general Trelliscode design criterion for reliable data transmission over noisy discrete memoryless channels", Proc. IEEE Internat. Symp. on Inform. Theory, Whistler, Canada, 1995, p. 68.
4. P. Bremaud, Point Processes and Queues, Springer, New York, 1981.
5. D. Clements and B.D.O. Anderson, "A non-linear fixed lag smoother for finite-state Markov processes", IEEE Trans. Inform. Theory, Vol. 21, July 1975, pp. 446-452.
6. I.B. Collings and V. Krishnamurthy, "On-line identification of hidden Markov models via recursive prediction error techniques", IEEE Trans. Signal Process., Vol. 42, No. 12, December 1994, pp. 3535-3539.
7. L. Deng, "A generalized hidden Markov model with state-conditioned trend functions of time for the speech signal", Signal Processing, Vol. 27, No. 1, April 1992, pp. 65-78.
8. R.J. Elliott, "New finite-dimensional filters and smoothers for noisily observed Markov chains", IEEE Trans. Inform. Theory, Vol. 39, No. 1, January 1993, pp. 265-271.
9. G.D. Forney Jr., "The Viterbi algorithm", Proc. IEEE, Vol. 61, No. 3, March 1973, pp. 268-278.
10. G. Kallianpur, Stochastic Filtering Theory, Springer, New York, 1980, Chapter IV.
11. L.R. Rabiner, "A tutorial on Hidden Markov Models and selected applications in speech recognition", Proc. IEEE, Vol. 77, No. 2, February 1989, pp. 257-285.
12. A. Segall, "Stochastic processes in estimation theory", IEEE Trans. Inform. Theory, Vol. 22, No. 3, May 1976, pp 275-286.
13. A. Segall, "Recursive estimation from discrete-time point processes", IEEE Trans. Inform. Theory, Vol. 22, No. 4, July 1976.
14. S.V. Vaseghi, "State duration modelling in Hidden Markov models", Signal Processing, Vol. 41, No. 1, March 1995, pp. 31-41.
15. W. M. Wonham, "Some applications of stochastic differential equations to optimal non-linear filtering", SIAM J. Control, Vol. 2, 1965, pp. 347-369.
16. C. Yang, Y. Bar-Shalom and C.F. Lin, "Discrete-time point process filter for mode estimation", IEEE Trans. Automat. Control, Vol. 37, No. 11, November 1992, pp. 1812-1816.