A QR-based least mean squares algorithm for adaptive parameter estimation

The optimum nonlinearly modified least-mean-square (ONM-LMS) algorithm has been shown to perform better than both the LMS and the normalized LMS algorithms. This paper proposes a QR-LMS adaptive parameter estimation algorithm that can perform significantly better than ONM-LMS. The performances of QR-LMS, including its numerical stability, error propagation property, and tracking ability, are analyzed. These properties are also verified with numerical examples.