Online Time-Resolved Reconstruction Method for Acoustic Tomography System

Acoustic tomography can deliver accurate quantitative reconstruction of the covered temperature distribution with low equipment cost. For the application of real-time temperature field monitoring, both the temporal resolution and reconstruction speed are of great significance. In this article, we developed a novel online time-resolved reconstruction (OTRR) method, which can improve temporal resolution to capture dynamic changes and accelerate the tomographic reconstruction process for online real-time monitoring. First, by exploiting the redundancy of the temporal information, a temporal regularization is designed based on adaptive auto aggressive (AR) model to reduce the required amount of time of flight (TOF) data per frame. A sliding overlapping window is applied to further improve the reconstruction accuracy. Second, recursive reconstruction process performs a sliding iteration over each data segment. For the reconstruction of each frame, the online computation is noniterative. Numerical simulation and lab-scale experiment are performed to validate the proposed OTRR method. The reconstruction images are compared with the OTRR methods based on the Kalman filter. The results show that our method can improve the temporal resolution and computational time and produce acceptable results.