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Bayesian estimation of chirplet signals by MCMC sampling

We address the problem of parameter estimation of chirplets which are chirp signals with Gaussian shaped envelopes. The procedure we propose is an extension of our previous work on estimation of chirp signals (Lin and Djuric, 2000), and it is based on MCMC sampling. For fast convergence of the Markov chain Monte Carlo (MCMC) sampling based method, a critical step is the initialization of the method Since the chirplets have finite durations and may or may not overlap in time, we propose initialization procedures for each of these cases. We have tested the method by extensive simulations and compared it with Cramer-Rao bounds. The obtained results have been excellent.