Multirate Kalman filtering for the data fusion of displacement and acceleration measurements

Andrew Smyth; Meiliang Wu

doi:10.1117/12.658731

11 April 2006 Multirate Kalman filtering for the data fusion of displacement and acceleration measurements

Andrew Smyth, Meiliang Wu

Proceedings Volume 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems; 61741G (2006) https://doi.org/10.1117/12.658731
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2006, San Diego, California, United States

Abstract

Many damage detection and system identification approaches benefit from the availability of both acceleration and displacement measurements. This is particularly true in the case of suspected nonlinear behavior and permanent deformations. In civil and mechanical structural modeling accelerometers are most often used, however displacement sensors, such as non-contact optical techniques as well as GPS-based methods for civil structures are becoming more common. It is suggested, where possible, to exploit the inherent redundancy in the sensor information and combine the collocated acceleration and displacement measurements in a manner which yields highly accurate motion data. This circumvents problematic integration of accelerometer data that causes lowfrequency noise amplification, and potentially more problematic differentiation of displacement measurements which amplify high-frequency noise. Another common feature of displacement based sensing is that the high frequency resolution is limited, and often relatively low sampling rates are used. In contrast, accelerometers are often more accurate for higher frequencies and thus higher meaningful sampling rates are often available. The fusion of these two data types must therefore combine data sampled at different frequencies. A multi-rate Kalman filtering approach is proposed to solve this problem. In addition, a smoothing step is introduced to obtain improved accuracy in the displacement estimate when it is sampled at lower rates than the corresponding acceleration measurement. Through trials with simulated data the procedure's effectiveness is shown to be quite robust at a variety of noise levels and relative sample rates for this practical problem.

Citation Download Citation

Andrew Smyth and Meiliang Wu "Multirate Kalman filtering for the data fusion of displacement and acceleration measurements", Proc. SPIE 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 61741G (11 April 2006); https://doi.org/10.1117/12.658731

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