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|Title:||Diagonally loaded normalised sample matrix inversion (LNSMI) for outlier-resistant adaptive filtering|
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|
|Conference Name:||IEEE International Conference on Acoustics, Speech and Signal Processing (2007 : Honolulu, Hawaii)|
|School/Discipline:||School of Electrical and Electronic Engineering|
|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|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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