A hybrid adaptive array minimizing the effects of the random weight vector errors
S. De Lin, Mourad Barkat · 2003
The authors propose a hybrid adaptive array that minimizes the effects of the random weight vector errors in the LMS (least-mean squares) array and the Applebaum array. The algorithm developed for adjusting the weighting factors did not require any prior knowledge of the variance of the random errors. The results are summarized. It is found that the hybrid array performs better than the Applebaum array or the LMS array since its output SINR (signal-to-interference-plus-noise) is always larger. In addition, it is less sensitive to the variance of random errors.>