Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

• Main Authors: Ionuț-Dorinel Fîciu, Cristian-Lucian Stanciu, Cristian Anghel, Camelia Elisei-Iliescu
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Applied Sciences
• Subjects: adaptive filters; dichotomous coordinate descent (DCD); recursive least-squares (RLS); system identification; tensor decomposition
• Online Access: https://www.mdpi.com/2076-3417/11/18/8656

Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.