Optimal Bayesian Estimation of the State of a Probabilistically Mapped Memory-Conditional Markov Process with Application to Manual Morse Decoding.
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This dissertation investigates the problem of automatic transcription of the hand-keyed Morse signal. A unified model for this signal process transmitted over a noisy channel is shown to be a system in which the state of the Morse process evolves as a memory-conditioned probabilistic mapping of a conditional Markov process, with the state of this process playing the role of a parameter vector of the channel model. The decoding problem is then posed as finding an optimal estimate of the state of the Morse process, given a sequence of measurements of the detected signal. The Bayesian solution to this nonlinear estimation problem is obtained explicitly for the parameter-conditional linear-gaussian channel, and the resulting optimal decoder is shown to consist of a denumerable but exponentially expanding set of linear Kalman filters operating on a dynamically evolving trellis. Decoder performance is obtained by computer simulation, for the case of random letter message texts.