Phase-Based Displacement Sensor With Improved Spatial Frequency Estimation and Data Fusion Strategy

Abstract

Phase-based vibration measurement has attra- cted a lot of attention due to its advantages of wireless, non-contact, and full-field measurement capabilities. The phase shifts across the different video frames correspond to the motion of the structure, which makes it possible to measure the vibration with the phase. However, the decoding of the phase shifts and the presence of the phase singularities make the phase-based method noise-sensitive and non-robust when concerning vibration measurement. Within this paper, an improved framework is proposed to enhance the performance of the phase-based technique for vibration measurement. A double filtering approach combined with the O’Shea refinement is introduced to decrease the phase noise to obtain accurate spatial frequency estimates. Then a confidence measure index is constructed to evaluate the reliability of phase responses to avoid the outliers induced by the phase singularities. Finally, an information fusion strategy over multiple scales is developed to enhance the accuracy and robustness of the motion estimation. Both simulated and experimental results verify the effectiveness and accuracy of the proposed framework.