Robust recursive total least squares algorithm

Jun-Seok Lim; Yong-Gook Pyeon

doi:10.7776/ASK.2024.43.6.624

All Issue

2024 Vol.43, Issue 6 Preview Page Next Page

Research Article

Robust recursive total least squares algorithm 견실한 재귀형 완전 최소 자승 알고리즘

30 November 2024. pp. 624-629

PDF XML

Abstract

It is known that the complete least squares method performs better estimation performance than the least squares method when noise is present in both input and output. When using the complete least squares method, a Recursive Total Last Squares (RTLS) algorithm has been proposed to add real-time performance. However, when using the RTLS algorithm, it has numerical instability resulting from inverse matrix calculation. In this paper, we propose a method to reduce the numerical instability of the algorithm structure of RTLS. For this algorithm, we propose a new RTLS algorithm that introduces a whitening process.

Keywords

Total least squares

Robust algorithm

Recursive Total Least Squares (RTLS)

Whitening

잡음이 입출력에 모두 존재하는 경우 완전 최소 자승법이 최소 자승법보다는 더 나은 추정 성능을 낸다는 것이 알려져 있다. 완전 최소 자승법을 사용할 경우 실시간 성을 더하기 위해서 Recursive Total Least Squares(RTLS) 알고리즘이 제안되어 있다. 그러나 RTLS 알고리즘을 사용할 때 역행렬 계산에서 오는 수치적인 불안정성을 지닌다. 본 논문에서는 RTLS의 알고리즘 구조의 수치적 불안정성을 줄이기 위한 방법을 제안한다. 이 알고리즘을 위해서 백색화 과정을 도입한 새로운 RTLS를 제안한다.

키워드

완전 최소 자승

견실화 알고리즘

Recursive Total Least Squares (RTLS)

백색화

References

C. Davila, "An efficient recursive total least squares algorithm for FIR adaptive filtering," IEEE Trans Signal Process, 42, 268-280 (1994).

10.1109/78.275601

N. Choi, J. Lim, J. Song, and K. Sung, "Adaptive system identification using an efficient recursive total least squares algorithm," J. Acoust. Soc. Kr. 22, 93-100 (2003).

L. Lu, H. Zhao, and B. Champagne, "Diffusion total least-squares algorithm with multi-node feedback," Signal Processing, 153, 243-254 (2018).

10.1016/j.sigpro.2018.07.025

L. Li, H. Zhao, and S. Lv, "Diffusion recursive total least square algorithm over adaptive networks and performance analysis," Signal Processing, 182, 1-10 (2021).

10.1016/j.sigpro.2020.107954

J. Lim and H. Pang, "l1-regularized recursive total least squares based sparse system identification for the error-in-variables," SpringerPlus, 5, 1-9 (2016).

10.1186/s40064-016-3120-627652035PMC5007238

A. H. Sayed, Fundamentals of Adaptive Filtering (Wiley, New York, 2003), pp. 212-280.

S. C. Douglas, "Numerically - robust o(n2) recursive least-squares estimation using least squares prewhitening," Proc. ICASSP, 412-415 (2000).

J. Lim, Y. Pyeon, and S. Kim, "L1 norm-recursive least squares algorithm for the robust sparse acoustic communication channel estimation," J. Acoust. Soc. Kr. 22, 32-37 (2020).

A. Hasso, K. Jacksi, and K. Smith, "Effect of quantization error and SQNR on the ADC using truncating method to the nearest integer bit," Proc. ICOASE, 112-117 (2019).

10.1109/ICOASE.2019.8723801

Information

Publisher :The Acoustical Society of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 43
No :6
Pages :624-629
Received Date : 2024-09-20
Revised Date : 2024-10-21
Accepted Date : 2024-11-06
DOI :https://doi.org/10.7776/ASK.2024.43.6.624

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

All Issue