A SELF-NORMALIZING GRADIENT SEARCH ADAPTIVE ARRAY ALGORITHM

A method is described to normalize the step parameter and perturbation amount used for gradient search adaptive algorithms. The step normalization assures rapid stable convergence. The perturbation normalization also assures rapid stable convergence and limits the output perturbation noise to a level below thermal noise. The normalizations are computationally simple and are consistent with the use of a single receiver at the adaptive array output. Results are presented to verify the robustness of the technique.