Data-Reuse Recursive Least-Squares Algorithms

被引：21

作者：

Paleologu, Constantin ^{[1
]}

Benesty, Jacob ^{[2
]}

Ciochina, Silviu ^{[1
]}

机构：

[1] Univ Politehn Bucuresti, Bucharest 060042, Romania

[2] Univ Quebec, INRS EMT, Montreal, PQ H5A 1K6, Canada

来源：

IEEE SIGNAL PROCESSING LETTERS | 2022年 / 29卷

关键词：

Signal processing algorithms; Convergence; Covariance matrices; Computational complexity; Indexes; Kalman filters; Jacobian matrices; Adaptive filters; echo cancellation; data-reuse; recursive least-squares (RLS) algorithm; fast RLS (FRLS) algorithm; convergence; tracking; VARIABLE FORGETTING FACTOR; FACTOR RLS ALGORITHM; NOISE;

D O I：

10.1109/LSP.2022.3153207

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：

There are different strategies to improve the overall performance of the recursive least-squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference signals) several times. First, we present a computationally efficient data-reuse RLS algorithm, which is the result of a low complexity implementation of the data-reuse process. Moreover, we extend the idea to the fast RLS algorithm. Simulations performed in the context of echo cancellation support the performance gain.

引用

页码：752 / 756

页数：5

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