Optimal Bayesian estimation of the state of a probabilistically mapped memory-conditional Markov process with application to manual Morse decoding
Bell, Edison Lee
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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 lineargaussian channel, and the resulting optimal decoder is shown to consist of a denumerable but exponentially expanding set of linear Kalman filters operating ona dynamically evolving trellis. Decoder performance is obtained by computer simulation, for the case of random letter message texts. For nonrandom texts, further research is indicated to specify linguistic and formatdependent models consistent with the model structure developed herein.
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