论文信息 - Set-Membership Binormalized Data-Reusing Algorithms

Set-Membership Binormalized Data-Reusing Algorithms

Abstract This paper presents new data selective adaptive filtering algorithms. The algorithms are derived under the concept of set-membership filtering (SMF) and also use the concept of data reusing. The algorithms include data-dependent step-sizes that provide fast convergence. The results show considerable performance improvement using the new algorithms for correlated input signals compared with the recently proposed set-membership NLMS (SM-NLMS) algorithm.

Stefan Werner | Paulo S. R. Diniz | P. Diniz | S. Werner

[1] J. Deller. Set membership identification in digital signal processing , 1989, IEEE ASSP Magazine.

[2] J. R. Deller,et al. Least-square identification with error bounds for real-time signal processing and control , 1993, Proc. IEEE.

[3] Shirish Nagaraj,et al. Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size , 1998, IEEE Signal Processing Letters.

[4] Y. F. Huang,et al. On the value of information in system identification - Bounded noise case , 1982, Autom..

[5] Yih-Fang Huang,et al. Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor , 1985, 1985 24th IEEE Conference on Decision and Control.

[6] Paulo S. R. Diniz,et al. Adaptive Filtering: Algorithms and Practical Implementation , 1997 .