Detection and estimation of DOA's of signals via Bayesian predictive densities

A new criterion based on Bayesian predictive densities and subspace decomposition is proposed for simultaneous detection of signals impinging on a sensor array and estimation of their direction-of-arrivals (DOAs). The solution is applicable for both coherent and noncoherent signals and an arbitrary array geometry. The proposed detection criterion is strongly consistent and outperforms the MDL and AIC criteria, particularly for a small number of sensors and/or snapshots, and/or low SNR, without increased computational complexity. When the prior of the direction-of-arrival is a uniform distribution, the Bayesian estimator for the directional parameters coincides with the unconditional maximum likelihood estimator. Simulation results that demonstrate the performance of the proposed solution are included.<
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