Abstract
Two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied. The first algorithm is referred to as the recursive LMS (least mean squares) and is applicable for a general array. The second algorithm is referred to as the improved LMS algorithm and exploits the Toeplitz structure of the array correlation matrix and can be used only for an equispaced linear array. >
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