Experimental antenna array calibration with artificial neural networks
Signal Processing
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In array processing applications, quadrature receiver gain imbalance can significantly degrade the performance of direction-finding algorithms and adversely affect the received signal quality. These errors are present, to some degree, when quadrature receivers are used with sensor arrays, and can be partially compensated for by estimating these error parameters and correcting for them during array calibration. Of particular interest is finding the best estimator possible to ensure proper and accurate calibration. This paper derives the maximum likelihood (ML) estimator for the quadrature gain errors, based on a measured array manifold. The bias and variance of the estimators are derived and simulations are incorporated to demonstrate estimator effectiveness.