An Array Recursive Least-Squares Algorithm With Generic Nonfading Regularization Matrix

摘要

We present a novel array RLS algorithm with forgetting factor that circumvents the problem of fading regularization, inherent to the standard exponentially-weighted RLS, by allowing for time-varying regularization matrices with generic structure. Simulations in finite precision show the algorithm's superiority as compared to alternative algorithms in the context of adaptive beamforming.