On FIR system identification from noisy input and output data

Wei Xing Zheng; Qiuqi Ruan; Baozong Yuan; Xiaofang Tang

On FIR system identification from noisy input and output data

Wei Xing Zheng
, Qiuqi Ruan
, Baozong Yuan
, Xiaofang Tang

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Conference Paper

Abstract

This paper is concerned with identifying parameters of finite impulse response (FIR) systems from noisy input-output data. The key idea is to estimate the input noise variance by minimizing a properly defined optimization criterion. Once a good estimate of the input noise variance is available, the unbiased estimates of the FIR system parameters are readily obtained by a closed-form least-squares solution without involving any iteration process. The proposed modified least-squares algorithm is compared with other existing methods through computer simulations.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Signal Processing and Communication, held in Beijing, China, 26-29 October, 2008
Publisher	IEEE
Number of pages	4
ISBN (Print)	9781424421794
Publication status	Published - 2008
Event	IEEE International Conference on Signal Processing and Communication - Duration: 1 Jan 2008 → …

Conference

Conference	IEEE International Conference on Signal Processing and Communication
Period	1/01/08 → …

Keywords

signal processing
adaptive filters
noise
algorithms
least squares
parameter estimation

Cite this

@inproceedings{88ce25a8806d42fab29f2cce6c7c8775,

title = "On FIR system identification from noisy input and output data",

abstract = "This paper is concerned with identifying parameters of finite impulse response (FIR) systems from noisy input-output data. The key idea is to estimate the input noise variance by minimizing a properly defined optimization criterion. Once a good estimate of the input noise variance is available, the unbiased estimates of the FIR system parameters are readily obtained by a closed-form least-squares solution without involving any iteration process. The proposed modified least-squares algorithm is compared with other existing methods through computer simulations.",

keywords = "signal processing, adaptive filters, noise, algorithms, least squares, parameter estimation",

author = "Zheng, \{Wei Xing\} and Qiuqi Ruan and Baozong Yuan and Xiaofang Tang",

year = "2008",

language = "English",

isbn = "9781424421794",

booktitle = "Proceedings of the 9th International Conference on Signal Processing and Communication, held in Beijing, China, 26-29 October, 2008",

publisher = "IEEE",

note = "IEEE International Conference on Signal Processing and Communication ; Conference date: 01-01-2008",

}

TY - GEN

T1 - On FIR system identification from noisy input and output data

AU - Zheng, Wei Xing

AU - Ruan, Qiuqi

AU - Yuan, Baozong

AU - Tang, Xiaofang

PY - 2008

Y1 - 2008

N2 - This paper is concerned with identifying parameters of finite impulse response (FIR) systems from noisy input-output data. The key idea is to estimate the input noise variance by minimizing a properly defined optimization criterion. Once a good estimate of the input noise variance is available, the unbiased estimates of the FIR system parameters are readily obtained by a closed-form least-squares solution without involving any iteration process. The proposed modified least-squares algorithm is compared with other existing methods through computer simulations.

AB - This paper is concerned with identifying parameters of finite impulse response (FIR) systems from noisy input-output data. The key idea is to estimate the input noise variance by minimizing a properly defined optimization criterion. Once a good estimate of the input noise variance is available, the unbiased estimates of the FIR system parameters are readily obtained by a closed-form least-squares solution without involving any iteration process. The proposed modified least-squares algorithm is compared with other existing methods through computer simulations.

KW - signal processing

KW - adaptive filters

KW - noise

KW - algorithms

KW - least squares

KW - parameter estimation

UR - http://handle.uws.edu.au:8081/1959.7/45290

M3 - Conference Paper

SN - 9781424421794

BT - Proceedings of the 9th International Conference on Signal Processing and Communication, held in Beijing, China, 26-29 October, 2008

PB - IEEE

T2 - IEEE International Conference on Signal Processing and Communication

Y2 - 1 January 2008

ER -

On FIR system identification from noisy input and output data

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this