Open AccessOutlier‐resistant adaptive filtering based on sparse Bayesian learning
Author(s) -
Zhu Wei,
Tang Jun,
Wan Shuang,
Zhu JieLi
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2014.0238
Subject(s) - outlier , maximum a posteriori estimation , covariance matrix , computer science , adaptive filter , noise (video) , artificial intelligence , pattern recognition (psychology) , filter (signal processing) , bayesian probability , covariance , algorithm , a priori and a posteriori , estimation of covariance matrices , machine learning , mathematics , statistics , computer vision , maximum likelihood , philosophy , epistemology , image (mathematics)
In adaptive processing applications, the design of the adaptive filter requires estimation of the unknown interference‐plus‐noise covariance matrix from secondary training data. The presence of outliers in the training data can severely degrade the performance of adaptive processing. By exploiting the sparse prior of the outliers, a Bayesian framework to develop a computationally efficient outlier‐resistant adaptive filter based on sparse Bayesian learning (SBL) is proposed. The expectation–maximisation (EM) algorithm is used therein to obtain a maximum a posteriori (MAP) estimate of the interference‐plus‐noise covariance matrix. Numerical simulations demonstrate the superiority of the proposed method over existing methods.