Sensor-array calibration using a maximum-likelihood approach

High-resolution array processing algorithms for source localization are known to be sensitive to errors in the model for the sensor-array spatial response. In particular, unknown gain, phase, and mutual coupling as well as errors in the sensor positions can seriously degrade the performances of array-processing algorithms. This paper describes a calibration algorithm that estimates the calibration matrix consisting of the unknown gain, phase, and mutual-coupling coefficients as well as the sensor positions using a set of calibration sources in known locations. The estimation of the various parameters is based on a maximum likelihood approach. Cramer-Rao lower-bound (CRB) expressions for the sensor positions and the calibration matrix parameters are also derived. Numerical results are shown to illustrate the potential usefulness of the proposed calibration algorithm toward better accuracy and resolution in parametric array-processing algorithms.

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