Accession Number:

ADA046503

Title:

Optimal Bayesian Estimation of the State of a Probabilistically Mapped Memory-Conditional Markov Process with Application to Manual Morse Decoding.

Descriptive Note:

Doctoral thesis,

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CALIF

Personal Author(s):

Report Date:

1977-09-01

Pagination or Media Count:

196.0

Abstract:

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.

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE