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Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2002

Classification of chirp signals using hierarchical bayesian learning and MCMC methods

Résumé

This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm.
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hal-03604478 , version 1 (10-03-2022)

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Manuel Davy, Christian Doncarli, Jean-Yves Tourneret. Classification of chirp signals using hierarchical bayesian learning and MCMC methods. IEEE Transactions on Signal Processing, 2002, 50 (2), pp.377-388. ⟨10.1109/78.978392⟩. ⟨hal-03604478⟩
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