Joint Bayesian estimation of DOA and frequency of BPSK (binary phase shift keying) signals in α-stable noise

The signal processing literature has traditionally been dominated by Gaussian noise model assumptions. In this paper, we present a Bayesian approach for DOA (Direction of Arrival) and frequency estimation of narrow band signals in additive α-stable noise. Approximating the α-stable noise by Gaussian mixture enables us to use the Bayesian techniques to derive a posterior probability density for DOA and frequency parameters from the signal and noise models. This posterior probability is then used in the Metropolis-Hasting algorithm to derive samples for the DOA and frequency parameters. The mean square errors (MSEs) of the parameters are compared with ROC-MUSIC (Robust Covariation MUSIC) algorithm. This new algorithm can be used with a significantly lower number of received data to estimate the parameters