A compressive sensing method for processing and improving vision-based target-tracking signals for structural health monitoring

Monitoring large structures using a vision-based target-tracking (TT) system while maintaining the full resolution of high-speed cameras may limit the data size or the sampling rate selection. Also, similar to wireless sensors networks where data loss often occurs during data transmission, TT signals could possibly lose data due to overexposure. The overall goal of this paper is to demonstrate the validity of compressive sensing (CS) for TT time signal processing when faced with challenges such as data loss. The first part of the study is concerned with signal length where TT signals could be further compressed while original data length is already minimum. Next, CS is investigated for improving and recovering TT signals from the possibility of signal loss as well as enhancing sampling rate for system identification purposes. Two case studies were used to obtain the signals for CS processing. The first case included four field-monitoring tests of a footbridge that were conducted using different sampling rates TT with limited data length to use full cameras resolution. The second case study was concerned with measuring the shifting of the modal properties of a large-scale bridge model from white-noise excitation after earthquake loading. The results show that with limited signal length, lower sampling rates, or data loss, CS techniques can successfully improve TT signals.

Automated summary

This study investigates the effectiveness of compressive sensing (CS) in processing TT time signals, particularly in the face of challenges such as data loss. Through two case studies, the results show that CS techniques can successfully improve TT signals even with limited signal length, lower sampling rates, or data loss.