Dynamic displacement estimation by fusing LDV and LiDAR measurements via smoothing based Kalman filtering

This paper presents a smoothing based Kalman filter to estimate dynamic displacement in real-time by fusing the velocity measured from a laser Doppler vibrometer (LDV) and the displacement from a light detection and ranging (LiDAR). LiDAR can measure displacement based on the time-of-flight information or the phase-shift of the laser beam reflected off form a target surface, but it typically has a high noise level and a low sampling rate. On the other hand, LDV primarily measures out-of-plane velocity of a moving target, and displacement is estimated by numerical integration of the measured velocity. Here, the displacement estimated by LDV suffers from integration error although LDV can achieve a lower noise level and a much higher sampling rate than LiDAR. The proposed data fusion technique estimates high-precision and high-sampling rate displacement by taking advantage of both LiDAR and LDV measurements and overcomes their limitations by adopting a real-time smoothing based Kalman filter. To verify the performance of the proposed dynamic displacement estimation technique, a series of lab-scale tests are conducted under various loading conditions.