DETECTION AND ESTIMATION OF DOAS OF SIGNALS VIA BAYESIAN PREDICTIVE DENSITIES

A new criterion based on Bayesian predictive densities and subspace de composition is proposed for simultaneous detection of signals impingin g on a sensor array and estimation of their direction-of-arrivals (DOA 's). The solution is applicable for both coherent and noncoherent sign als and an arbitrary array geometry. The proposed detection criterion is strongly consistent and outperforms the MDL and AIC criteria, parti cularly for a small number of sensors and/or snapshots, and/or low SNR , without increased computational complexity. When the prior of the di rection-of-arrival is a uniform distribution, the Bayesian estimator f or the directional parameters coincides with the unconditional maximum likelihood estimator. Simulation results that demonstrate the perform ance of the proposed solution are included.