Capabilitiy of array processing algorithms to estimate source bearings

The capabilities of classical, minimum energy, and linear predictive array processing algorithms to estimate the bearings of two equal-energy sources is examined. Signal coherence is shown to affect adversely the resolution and detection capabilities, as well as the bias characteristics, of all three algorithms. For linear arrays of equally spaced sensors, the superior resolution capability of the linear predictive algorithm is demonstrated. The value of utilizing prediction elements in the center of the array to resolve very closely spaced source bearings is demonstrated. However, the linear predictive algorithm is least capable of detecting highly coherent sources. A tradeoff is established between resolving capability and sensitivity to finite averaging. Conditions are established which indicate which algorithm is best suited to anticipated levels of signal coherence and averaging. The estimates of source bearing produced by each algorithm are shown to be asymptotically biased. The bias produced by the classical beamformer is most severe, while the minimum energy beamformer produces the least bias.