Visible to the public Outlier-resistant adaptive filtering based on sparse Bayesian learning

TitleOutlier-resistant adaptive filtering based on sparse Bayesian learning
Publication TypeJournal Article
Year of Publication2014
AuthorsWei Zhu, Jun Tang, Shuang Wan, Jie-Li Zhu
JournalElectronics Letters
Volume50
Pagination663-665
Date PublishedApril
ISSN0013-5194
Keywordsadaptive filters, adaptive processing applications, Bayes methods, Covariance matrices, EM algorithm, expectation-maximisation algorithm, filtering theory, interference (signal), learning (artificial intelligence), MAP estimation, maximum a posteriori estimation, outlier-resistant adaptive filtering, SBL, secondary training data, sparse Bayesian learning, unknown interference-plus-noise covariance matrix estimation
Abstract

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.

URLhttp://ieeexplore.ieee.org/document/6809283/
DOI10.1049/el.2014.0238
Citation Key6809283