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 paper, we developed a novel online time-resolved reconstruction (OTRR) methods, which can improve temporal resolution to capture dynamic changes and accelerate the tomographic reconstruction process for online real-time monitoring. Firstly, by exploiting the redundancy of the temporal information, a temporal regularisation is designed based on adaptive auto aggressive (AR) model to reduce the required amount of TOF data per frame. A sliding overlapping window is applied to further improve the reconstruction accuracy. Secondly, recursive reconstruction process performs a sliding iteration over each data segment. For the reconstruction of each frame, the online computation is non-iterative. Numerical simulation and lab-scale experiment are performed to validate the proposed OTRR method. The reconstruction images are compared with the online time-resolved reconstruction methods based on Kalman filter. Results show that our method can improve the temporal resolution the computational time and produce acceptable results.