The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

L1 norm-recursive least squares algorithm for the robust sparse acoustic communication channel estimation

희소성 음향 통신 채널 추정 견실화를 위한 백색화를 적용한 l1놈-RLS 알고리즘

  • 임준석 (세종대학교 전자정보통신공학과) ;
  • 편용국 (강원도립대학 로봇스마트팩토리과) ;
  • 김성일 (국방과학연구소)
  • Received : 2019.10.10
  • Accepted : 2019.11.18
  • Published : 2020.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a new l1-norm-Recursive Least Squares (RLS) algorithm which is numerically more robust than the conventional l1-norm-RLS. The l1-norm-RLS was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm has numerical instability in the inverse matrix calculation. In this paper, we propose a new algorithm which is robust against the numerical instability. We show that the proposed method improves stability under several numerically erroneous situations.

본 논문은 l1놈-Recursive Least Squares(RLS)에 수치 계산상 견실화를 더한 새로운 알고리즘을 제안한다. Eksioglu와 Tanc는 희소성 음향 채널 추정을 위해서 l1놈-RLS 알고리즘을 구현하였다. 그러나 이 알고리즘의 근간인 RLS 계산법 역행렬 계산에서 수치 계산상의 불안정성을 지니고 있다. 본 논문에서는 이런 불안정성을 낮추는 새로운 알고리즘을 제안한다. 그리고 제안한 방법을 사용했을 때 수치적 불안정성에 대한 성능이 개선되었음을 보인다.

Keywords

References

  1. A. H. Sayed, Fundamentals of Adaptive Filtering (Wiley, NewYork, 2003), pp. 212-280.
  2. J. S. Lim, "A constant modulus algorithm based on an orthogonal projection" (in Korean), J. Acoust. Soc. Kr. 28, 640-645 (2009).
  3. J. S. Lim and Y. G. Pyeon, "A constant modulus algorithm for blind acoustic communication channel equalization with improved convergence using switching between projected CMA and algebraic step size CMA" (in Korean), J. Acoust. Soc. Kr. 34, 394-402 (2015). https://doi.org/10.7776/ASK.2015.34.5.394
  4. M. Kocic, D. Brady, and M. Stojanovic, "Sparse equalization for real-time digital underwater acoustic communications," Proc. OCEANS'95, 1417-1422 (1995).
  5. W. F. Schreiber, "Advanced television systems for terrestrial broadcasting: Some problems and some proposed solutions," Proc. IEEE, 83, 958-981 (1995). https://doi.org/10.1109/5.387095
  6. W. U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, "Compressed channel sensing," Proc. CISS. 5-10 (2008).
  7. E. J. Candes and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag. 25, 21-30 (2008). https://doi.org/10.1109/MSP.2007.914731
  8. C. R. Berger, Z. Wang, J. Huang, and S. Zhou, "Application of compressive sensing to sparse channel estimation," IEEE Commun. Mag. 48, 164-174 (2010).
  9. Y. Gu, J. Jin, and S. Mei, "l0 norm constraint LMS algorithm for sparse system identification," IEEE Signal Process. Lett. 16, 774-777 (2009). https://doi.org/10.1109/LSP.2009.2024736
  10. Y. Chen, Y. Gu, and A. O. Hero, "Sparse LMS for system identification," Proc. ICASSP. 3125-3128 (2009).
  11. E. M. Eksioglu and A. K. Tanc, "RLS algorithm with convex regularization," IEEE Signal Processing Lett. 18, 470-473 (2011). https://doi.org/10.1109/LSP.2011.2159373
  12. S. C. Douglas, "Numerically - Robust. O(N2) recursive least-squares estimation using least squares prewhitening," Proc. ICASSP. 412-415 (2000).