Acoustic tomography imaging for atmospheric temperature and wind velocity field reconstruction
Owing to its non-invasive nature, fast imaging speed, low equipment cost, scalability for a variety of measurement ranges, and ability to simultaneously monitor both temperature and wind velocity fields, acoustic tomography has attracted considerable interest in the field of atmospheric imaging. This thesis aims to improve the reconstruction quality of the acoustic tomography system for temperature and wind velocity field imaging. Focusing on this goal, the contribution of the thesis can be summarised from the perspectives of data collection system development, robust and accurate TOF estimation method, and high-quality scalar and vector tomographic image reconstruction methods for temperature and wind velocity fields respectively. Details are given below. Firstly, in order to facilitate the experimental study of acoustic tomography imaging, the design and evaluation of the data collection system and TOF estimation method was presented. The evaluation results indicate that the presented data acquisition system and TOF estimation method has good quantitative accuracy in the lab-scale experiments. The temporal resolution is of great significance for the real-time monitoring of the fast-changing temperature field. To improve the temporal resolution, a novel online time-resolved reconstruction (OTRR) method is presented, which can reconstruct high quality time-resolved images by using fewer TOFs per frame. Compared to state-of-the-art dynamic reconstruction algorithms such as the Kalman filter reconstruction, the proposed algorithm demonstrated superior spatial resolution and preferable quantitative accuracy in the reconstructed images. These features are necessary for the real-time monitoring of the fast-changing temperature field. The forward modelling of most acoustic tomography problems is based on a straight ray model, which may result in large modelling errors due to the refraction effect under a large gradient temperature field. In order to reduce the inaccuracy of using the straight ray model, a bent ray model and nonlinear reconstruction algorithm is applied, which allows the sound propagation ray paths and temperature distribution to be reconstructed iteratively from the TOFs. Using acoustic tomography to reconstruct large-scale temperature and wind velocity fields, a fully parallel TOF measurement scheme is necessary. To achieve this goal, a set of orthogonal acoustic waveforms based on the filtered and modulated Kasami sequence is designed and a cross-correlation based TOF estimation method is used for data collection. Besides, to overcome the invisible field problem and improve the image quality of the wind velocity reconstruction, a divergence-free regularised vector tomographic reconstruction algorithm is studied. The proposed method is able to provide accurate tomographic reconstruction of the 2D horizontal wind velocity field from the TOF measurements. In summary, this thesis focuses on the improvement of acoustic tomography techniques for temperature and wind velocity fields, including the phase corrected Akaike information criterion (AIC) TOF estimation for accurate and robust TOF estimation, the online time-resolved reconstruction method for real-time monitoring of the fast changing temperature field, the nonlinear reconstruction based on the bent ray model to reconstruct the temperature field with a large gradient, and the divergence-free regularised reconstruction method to visualise the 2D horizontal wind velocity field.