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email vibergaechalmersse Modern techniques for sensor array signal processing are hampered by the often un realistic and overly simplistic assumptions used in their development Foremost among these assumptions are those relating to the array geometry or response including for example known sensor positions availability of complete gainphasemutual coupling calibration data uniform linear arrays etc Of course with real arrays none of these assumptions hold exactly Deviations from the nominal model occur due to environmen tal factors quantization eects perturbations in the locations of the antenna elements etc If such model errors are ignored serious performance degradation can result While in any given application the exact value of the perturbation to the array is unknown the size or distribution of such perturbations may be well understood Consequently in this paper a Bayesian approach is adopted to show how information in the form of an a priori distribution on the array model errors can be used to improve both direction of arrival and beamforming performance The general maximum a posteriori estimator for the problem is formulated and a computationally attractive alternative based on the concept of subspace tting is proposed The algorithms statistical performance for both DOA estimation and beamforming is evaluated by means of some simulation example