Adaptive noise variance identification for data fusion using subspace-based technique

Zhen Li; C. C. Chang

doi:10.1117/12.847353

31 March 2010 Adaptive noise variance identification for data fusion using subspace-based technique

Zhen Li, C. C. Chang

Proceedings Volume 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010; 76471U (2010) https://doi.org/10.1117/12.847353
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2010, San Diego, California, United States

Abstract

The Kalman filter is commonly employed to fuse the measured information from displacement and acceleration responses. This fusion technique can mitigate the noise effect and produce more accurate response estimates. The fusion performance however significantly depends on the accuracy of noise estimation. The noise variances are generally estimated empirically a priori and assumed to be fixed throughout the calculation. Hence some estimation error might occur when the noise characteristics are time-varying. In this study, an adaptive subspace-based technique is developed to identify the noise variances. The approach is based on the principal component analysis which decomposes noisecontaminated signals into the signal subspace and the noise subspace. The variances of the noise and signals can then be estimated independently. To track the time variations of the noise and signal variances in an on-line fashion, a projection approximation subspace tracking technique is employed. The proposed technique can be incorporated into an adaptive Kalman filter and provide a more accurate estimation for data fusion.

Citation Download Citation

Zhen Li and C. C. Chang "Adaptive noise variance identification for data fusion using subspace-based technique", Proc. SPIE 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, 76471U (31 March 2010); https://doi.org/10.1117/12.847353

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