A Bayesian approach to direction finding with parametric array uncertainty

With few exceptions, high-resolution source localization algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase response, mutual coupling, and receiver equipment effects. In practice, all such information is inevitably subject to errors. Recently, several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modeling errors. The technique proposed herein is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. The optimal maximum a posteriori (MAP) estimator for the problem at hand is formulated, and a more computationally attractive large-sample approximation is derived. In addition, the performance advantage of the algorithm is illustrated by an example involving a linear array mounted on a flexible structure.<
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