@inproceedings{716049290f27458dab59a09efd797aa7,
title = "Bias-compensated LMS estimation for adaptive noisy FIR filtering",
abstract = "We investigate the parameter estimation of adaptive FIR filter with noisy input. When the filter input is corrupted by additive noise, the parameter estimation of traditional LMS algorithm is biased. For the noise is unknown, we propose an input noise variance estimation method under unconstrained condition based on the LMS algorithm and combine the bias-compensated LMS (BCLMS) algorithm by removing the noise-induced bias. Simulation results show that our proposed algorithm has better estimate accuracy than the bias-compensated LMS algorithm with the input noise variance estimation under constrained condition, and provides an unbiased estimate of the filter parameters under any different noise levels.",
keywords = "Noise variance estimation, adaptive filtering, bias-compensated, least-mean-square (LMS), parameter estimation",
author = "Xu Tingting and Jia Lijuan and Kanae Shunshoku",
note = "Publisher Copyright: {\textcopyright} 2015 The Society of Instrument and Control Engineers-SICE.; 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015 ; Conference date: 28-07-2015 Through 30-07-2015",
year = "2015",
month = sep,
day = "30",
doi = "10.1109/SICE.2015.7285384",
language = "English",
series = "2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "81--85",
booktitle = "2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015",
address = "United States",
}