Joint DOA, frequency and model order estimation in additive α-stable noise

Many classes of noise encountered in real-life exhibit outliers that will not fit into a Gaussian noise model. α-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 α-stable noise. Approximating α-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