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A personal history of the Viterbi algorithm
IEEE Signal Processing Magazine, 2006 This paper describes how Andrew J. Viterbi developed a non-sequential decoding algorithm which proved useful in showing the superiority of convolutional codes over block codes for a given degree of decoding complexity. The Viterbi algorithm is now used in most digital cellular phones and digital satellite receivers as well as in such diverse fields as ...
A J Viterbi
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Proceedings of the IEEE, 1973
The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digital communications can be cast in this form.
G D Forney
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The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digital communications can be cast in this form.
G D Forney
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On the Viterbi decoding algorithm
IEEE Transactions on Information Theory, 1969A new interpretation of the Viterbi decoding algorithm based on the state-space approach to dyamical systems is presented. In this interpretation the optimum decoder solves a generalized regulator control problem by dynamic programming techniques.
J Omura
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The Viterbi algorithm for sparse channels
IEEE Transactions on Communications, 1996This paper presents a reduced complexity Viterbi algorithm for use as sequence estimator for linear intersymbol interference channels with coarsely located coefficients. In particular, the complexity does not depend on the channel impulse response length but only on the number of nonzero coefficients. No approximations are used in the algorithm.
N Benvenuto, R Marchesani
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Robustifying the Viterbi Algorithm
, 2014 We present an efficient algorithm for estimating hidden state sequences in imprecise hidden Markov models (iHMMs), based on observed output sequences. The main difference with classical HMMs is that the local models of an iHMM are not represented by a single mass function, but rather by a set of mass functions.
Cedric De Boom +3 more
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Implementing the Viterbi algorithm
IEEE Signal Processing Magazine, 1995The Viterbi algorithm, an application of dynamic programming, is widely used for estimation and detection problems in digital communications and signal processing. It is used to detect signals in communication channels with memory, and to decode sequential error-control codes that are used to enhance the performance of digital communication systems ...
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Iterative Viterbi Algorithm: Implementation Issues
IEEE Transactions on Wireless Communications, 2004 We address several issues for implementing the iterative Viterbi decoder. We show that 3-bit branch metric quantization, 7- or 8-bit state metric precision, and a survivor length of five times the constraint length yields little degradation for the iterative Viterbi algorithm (IVA).
Wei, Lei
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Experiments in Text Recognition with the Modified Viterbi Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979In this paper a modification of the Viterbi algorithm is formally described, and a measure of its complexity is derived. The modified algorithm uses aheuristic to limit the search through a directed graph or trellis. The effectiveness of the algorithm is investigated via exhaustive experimentation on an input of machine-printed text.
R Shinghal, Godfried T Toussaint
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Deformed template matching by the Viterbi algorithm
Pattern Recognition Letters, 1996Abstract We consider a template matching algorithm that aims to deform a given template and place it onto a target-image to match as many edges as possible. To simplify this optimization problem, we will introduce a particular class of deformations that makes the Viterbi Algorithm applicative.
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Marginalized Viterbi algorithm for hierarchical hidden Markov models [PDF]
Pattern Recognition, 2013 application/pdfThe generalized Viterbi algorithm, a direct extension of the Viterbi algorithm for hidden Markov models (HMMs), has been used to find the most likely state sequence for hierarchical HMMs.
Kazunori Iwata
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