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Type: Conference paper
Title: Diagonally loaded normalised sample matrix inversion (LNSMI) for outlier-resistant adaptive filtering
Author: Abramovich, Yuri
Spencer, Nicholas K.
Citation: IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2007, 15-20 April, 2007: vol. 3, pp. III-1105-III-1108
Publisher: IEEE
Issue Date: 2007
ISBN: 1424407281
Conference Name: IEEE International Conference on Acoustics, Speech and Signal Processing (2007 : Honolulu, Hawaii)
School/Discipline: School of Electrical and Electronic Engineering
Statement of
Abramovich, Y.I. and Spencer, N.K.
Abstract: Instead of a "hard" decision on ignoring "outlier" training samples in constructing the covariance matrix estimate, we propose a "softer" method that reduces the impact of such abnormal data samples on adaptive filter performance. Specifically, we introduce a diagonally loaded covariance matrix estimate that is normalised by a generalised inner product (GIP), which is more robust against outliers. We demonstrate the efficiency of this technique on high-frequency (HF) over-the-horizon radar (OTHR) data.
Rights: © Copyright 2007 IEEE – All Rights Reserved
RMID: 0020076104
DOI: 10.1109/ICASSP.2007.366877
Appears in Collections:Electrical and Electronic Engineering publications

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