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of the channel. The Viterbi algorithm is a popular method used to decode convolutionally coded messages. The algorithm tracks down the most likely state sequences the encoder went through in encoding the message, and uses this information to determine the original message. Instead of estimating a message based

on the computationally efficient Viterbi algorithm which is shown to achieve better perfor-mance than competing algorithms such as Orthogonal Matching

The Viterbi algorithm, derived using dynamic programming techniques, is a maxi-mum a posteriori (MAP) decoding method which was developed in the electrical engineering literature to be used in the analysis of hidden Markov models (HMMs). Given a particular HMM, the original algorithm recovers

The story of the Viterbi algorithm (VA) is told from a personal perspective. Applications both within and beyond communications are discussed. In brief summary, the VA has proved to be an extremely important algorithm in a surprising variety of fields. 1

A novel compound code is designed for the popular multi-dimensional (M-D) Wei trellis code [1] combined with a simple parity-check code. Using the iterative Viterbi decoding algorithm, we can achieve a remarkable performance improvement with low computational complexity. Simulation results show

Hierarchical problem abstraction, when applicable, may offer exponential reductions in computational complexity. Previous work on coarse-to-fine dynamic programming (CFDP) has demonstrated this possibility using state abstraction to speed up the Viterbi algorithm. In this paper, we show how

detector (MLSD) and the optimal maximum a posteriori (MAP) sequence detector, extending to the correlated noise case the Viterbi algorithm. We show that, when the signal-dependent noise is conditionally Gauss–Markov, the branch metrics in the MLSD are computed from the conditional second-order noise

This paper deals with the problem of finding the optimal se-quence of sub-word unit HMMs for a number of given utter-ances of a word. For this problem we present a new solution based on an extension of the Viterbi algorithm which maxi-mizes the joint probability of the utterances and all possible