A finite wordlength analysis of an LMS-Newton adaptive filtering algorithm

Author

De Campos, Marcello L R ; Diniz, Paulo S R ; Antoniou, Athanasios

Author_Institution

Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada

fYear

1993

fDate

3-6 May 1993

Firstpage

870

Abstract

The effects of quantization in an least-mean-square (LMS)-Newton adaptive filtering algorithm are investigated. The algorithm considered uses an optimum convergence factor that forces the output a posteriori error to become zero in each iteration. The propagation of errors due to quantization in the internal variables of the algorithm is investigated, and a closed-form formula for the excess mean square error due to quantization is derived. Fixed-point arithmetic is assumed throughout. Several simulations confirm the accuracy of the formulas presented

Keywords

Newton method; adaptive filters; convergence; digital arithmetic; filtering theory; least mean squares methods; quantisation (signal); roundoff errors; LMS-Newton adaptive filtering algorithm; closed-form formula; error propagation; excess mean square error; finite wordlength analysis; optimum convergence factor; quantization; Adaptive filters; Algorithm design and analysis; Convergence; Equations; Filtering algorithms; Fixed-point arithmetic; Mean square error methods; Noise measurement; Quantization; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-1281-3

Type

conf

DOI

10.1109/ISCAS.1993.393862

Filename

393862