Multi-Layered Recursive Least Squares for Time-Varying System Identification

被引：15

作者：

Towliat, Mohammad ^{[1
]}

Guo, Zheng ^{[2
]}

Cimini, Leonard J. ^{[1
]}

Xia, Xiang-Gen ^{[1
]}

Song, Aijun ^{[2
]}

机构：

[1] Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA

[2] Univ Alabama, Dept Elect & Comp Engn, Tuscaloosa, AL 35487 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2022年 / 70卷

基金：

美国国家科学基金会;

关键词：

Echo cancellation; mean square error; recursive least squares; system identification; time-varying systems; ADAPTIVE-FILTERING ALGORITHM; TRANSVERSAL FILTERS; IMPULSIVE-NOISE; RLS; PERFORMANCE; LMS;

D O I：

10.1109/TSP.2022.3170708

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, the RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered RLS (m-RLS) estimator to address this concern. The m-RLS estimator is composed of multiple RLS estimators, each of which is employed to estimate and eliminate the misadjustment of the previous layer. It is shown that the mean squared error (MSE) of the m-RLS estimate can he minimized by selecting the optimum number of layers. We provide a method to determine the optimum number of layers. A low-complexity implementation of m-RLS is discussed and it is indicated that the complexity order of the proposed estimator can he reduced to O(M), where M is the IR length. Through simulations, we show that m-RLS outperforms the classic RLS and the RLS methods with a variable forgetting factor.

引用

页码：2280 / 2292

页数：13

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