RIA: Self-Adaptive Maximum-Likeihood Sequence Estimation

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9309044 Paris The Principal Investigator is addressing the estimation of the most likely state sequence of a discrete-time finite-state Markov process with unknown parameters observed in independent noise. A maximum likelihood criterion over both the input sequence and the parameters is introduced for estimating the state sequence without using an embedded training sequence. Asymptotically, this estimator is expected to be close to the maximum-likelihood sequence estimator with completely known parameters. To facilitate the search for the most likely state sequence, the investigator is introducing computationally simple algorithms which are guaranteed to converge. Besides developing the underlying theory and algorithms, the research is focussing on applications to digital mobile radio communication systems. The award provides support for three years.