Direction-of-arrival and frequency estimation using Poisson-Gaussian modeling

We address the problem of identification of sinusoidal components from observed data, which is fundamental for array signal processing and spectral line decomposition. Joint detection and estimation are proposed in a unified Bayesian framework, so that no preliminary estimate of the number of signals is required. All unknown quantities are estimated from a unique regularized “stochastic” likelihood function, including the number of sources and statistical parameters. The impulsive solution is modeled as a continuous Poisson-Gaussian process. A powerful iterative technique is proposed to maximize the posterior likelihood. Simulation results show that the method behaves particularly well for small data sets, even for a single experiment