Sequential MCMC for spatial signal separation and restoration from an array of sensors

This paper addresses the implementation of sequential Markov Chain Monte Carlo (MCMC) estimation, also known as particle filtering, to signal separation and restoration problems, using a passive array of sensors. This proposed method offers significant advantages: 1) the signals mixed at the array can be well-separated in space and restored in an online fashion, 2) the assumption of a stationary environment over the interval can be relaxed, 3) the estimated joint posterior distribution of all the unknown parameters can be used for statistical inference, and 4) the method can also be used to dynamically detect the number of signals throughout the observation period. The signals used in the simulation were mixed by a highly-nonlinear but structured steering-vector matrix. Simulation results demonstrated the effectiveness of the method in such a way that the true and restored signals were clearly separated and restored by the sequential MCMC method.