.png){kind=icon}
返回
Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2023.3276573.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper considers the problem of system parameter estimation using adaptive filter. Conventional adaptive algorithms will result in degraded performance in the presence of impulsive noise and biased estimation when the input signal is noisy. To address these issues, this paper proposes a robust bias-compensated least mean squares (R-BC-LMS) algorithm. It is derived by performing the maximum-a-posteriori estimation subject to a constraint on the squared norm of the weight vector difference, and then introducing an unbiasedness criterion to insert a bias compensation term in the update. Under common statistical assumptions, the mean and mean square behaviors of weight deviation are derived for the R-BC-LMS algorithm. In addition, we develop the estimator for the input and output noise variances. Simulations in channel estimation, vehicle handsfree echo cancellation, and direction-of-arrival estimation demonstrate that our method outperforms the competing algorithms.
Keyword:
Echo cancellation
direction-of-arrival estimation
impulsive interferences
least mean squares
noisy input
robust adaptive signal processing
spatial spectrum
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
A new combined-step-size normalized least mean square algorithm for cyclostationary inputs.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Active Power Control to Mitigate Frequency Deviations in Large-Scale Grid-Connected PV System Using Grid-Forming Single-Stage InvertersEnergies
OAAI
EnergiesIF0
OAAI分享收藏Changing Prevalence ofHelicobacter pyloriInfections in Korean Children with Recurrent Abdominal Pain
OAAI
OAAI分享收藏
查看更多内容