Adaptive Quantification of Noise Variance Using Subspace Technique
Publication: Journal of Engineering Mechanics
Volume 139, Issue 4
Abstract
Filters are commonly used in the postprocessing of noise-contaminated measurement signals for achieving noise removal or data fusion. Quantifying variance of noise embedded in the signal is an important task for the calibration and operation of filters. In this study, an adaptive subspace technique is proposed to identify and track noise variances with unknown and potentially time-varying characteristics from noise-contaminated signals. The technique is developed on the premise that the covariance of the clean signals is rank-deficient, i.e., the number of excited vibration modes is less than that of measured channels. After separating the vector space of noisy signals into a signal subspace and a noise subspace, noise variances are estimated using the energy of the noise subspace. The projection approximation subspace tracking technique is then employed to adaptively update the signal subspace and track the changes of noise variances from the measurement data. A numerical example of a 10-story building model and a field test on a pedestrian bridge are performed to validate the proposed technique. Results show that the proposed technique can identify and track time-sensitive noise variances accurately and efficiently from noisy measurement data. It is also illustrated that the proposed technique can be integrated into the Kalman filter for fusing acceleration and displacement data and provides a better displacement estimate when the data contain time-varying noises.
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Acknowledgments
This study is partly supported by Hong Kong Research Grant No. CERG 611409.
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Information & Authors
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Published In
Journal of Engineering Mechanics
Volume 139 • Issue 4 • April 2013
Pages: 469 - 478
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jun 23, 2011
Accepted: Jun 13, 2012
Published online: Jul 31, 2012
Published in print: Apr 1, 2013
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