Joint Bayesian Decomposition of a Spectroscopic Signal Sequence
Vincent Mazet · IEEE Signal Processing Letters · 2011
This letter addresses the problem of decomposing a sequence of spectroscopic signals: data are a series of (energy or electromagnetic) spectra and we aim to estimate the peak parameters (centers, amplitudes, and widths). The key idea is to perform the decomposition of the whole sequence and to impose the parameters to evolve smoothly through the sequence. The problem is set within a Bayesian framework whose posterior distribution is sampled using a Markov chain Monte Carlo simulated annealing algorithm. Simulations conducted on synthetic data illustrate the performance of the method.