On some detection and estimation problems in heavy-tailed noise
Signal Processing - Signal processing with heavy-tailed models
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Many classes of noise encountered in real-life exhibit outliers that will not fit into a Gaussian noise model. /spl alpha/-stable distributions are among the most important non-Gaussian models that can be used to accurately model impulsive noise environments. We introduce an algorithm that can be used to jointly estimate DOA (direction of arrival), frequency and model order in /spl alpha/-stable noise. Approximating /spl alpha/-stable noise by a Gaussian mixture, we use Bayesian principles to define a posterior density on the signal and noise parameter space. We describe an efficient stochastic algorithm called reversible jump RCMC (Markov chain Monte Carlo) that is used to evaluate our posterior density.