Maximum Likelihood Sequence Estimation for Hidden Reciprocal Processes

L.B. White

doi:10.1109/TAC.2013.2256012

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Maximum Likelihood Sequence Estimation for Hidden Reciprocal Processes

2013-10-01

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L.B. White *

DOI：10.1109/TAC.2013.2256012

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Abstract

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This paper addresses the problem of maximum likelihood sequence estimation (MLSE) based on a hidden reciprocal chain (HRC) as the underlying target model. HRCs are non-causal, discrete-time finite-state stochastic processes which can be regarded as the one-dimensional version of a Markov random field, although they are not in general Markov processes. This paper describes a procedure for evaluating the MLSE for a HRC and compares the resultant estimator with its Markov Model equivalent: the Viterbi algorithm. In addition, the performance of the newly proposed reciprocal MLSE is compared to a HRC-based optimal smoother.

Keywords:

Hidden Markov models

Markov random fields

maximum likelihood estimation

state estimation

stochastic systems

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