Bayesian array signal processing in additive generalized Gaussian noise

We present a Bayesian approach for DOA and frequency estimation of narrow band signals in additive generalized Gaussian noise. Using Bayesian techniques, the posterior probability densities for DOA (direction of arrival) and frequency parameters are derived from the signal and noise models. These posterior probabilities are then used in the Metropolis-Hastings (M-H) algorithm to derive the samples for the DOA and frequency parameters. The performances of our algorithms are studied by plotting the MSEs (mean square errors) of the parameters for various SNRs. The MSEs of the parameters are compared with the CRLBs (Cramer Rao lower bound) for the generalized Gaussian models.