Kalman filter based data fusion for dynamic displacement estimation using LDV and LiDAR
نویسندگان
چکیده
A real-time dynamic displacement estimation technique is developed by data fusion of laser Doppler vibrometer (LDV) and light dectection and ranging (LiDAR). The velocity measurement of LDV is low level of noise and sampled at high frequency, but has accumulated error during integration. Also, the LiDAR displacement measurement has high level of noise and low sampling frequency. The proposed technique combines the LDV velocity and LiDAR displacement measurements to estimate dynamic displacement with low noise level, high sampling frequency and no integration error. Kalman filter based smoothing algorithms are adopted to remove the accumulated error during the LDV velocity integration and the high noise of the LiDAR displacement in real time. To verify the estimation performance of the technique, a lab-scale test using a cantilever beam is performed.
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