Abstract
A hybrid adaptive array that combines the least mean square-error (LMS) array and the Applebaum array is presented. The array minimizes the effect of the random errors in the weight vectors of the LMS and Applebaum arrays. These weight vectors containing random errors are scaled and combined to yield a novel weight vector. The mean square error (MSE) is used as a measure of performance to derive optimal weighting factors. An algorithm is devised to adjust the weighting factors automatically by an iterative procedure based on the complex LMS algorithm to achieve the optimum weighting factors. It is shown that the hybrid array performs better than the Applebaum array or the LMS array. In addition, it is less sensitive to the random weight vector errors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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