一种声学层析成像温度分布高分辨率重建方法

A High Resolution Reconstruction Method of Temperature Distribution in Acoustic Tomography

准确测量温度分布对工业生产具有重要的意义。针对声学层析成像中有限的网格划分数目会影响重建精度的问题,提出TR-RBF(tikhonov regularization-radial basis function)重建算法对温度场进行高分辨率重建。采用Tikhonov正则化对超声飞行时间(time of flight,TOF)重建,得到粗网格下的温度分布,并用局部加权回归法对数据进行平滑处理,进而采用RBF神经网络将粗解进行预测得到细化后的温度分布。通过有噪声和无噪声的数值仿真,本算法与ART、SVD和Tikhonov三种算法相比,在典型峰型温度分布情况下的重建精度提升明显且抗噪性最好。

Accurate measurement temperature distribution is important for industrial production.In order to solve the number of mesh divisions will impact reconstruction accuracy in acoustic tomography,the TR-RBF(Tikhonov regularization-radial basis function)reconstruction algorithm is rebuilt to reconstruct the temperature field with high resolution.The Tikhonov regularization is used to reconstruct the ultrasound time of flight(TOF)to obtain a temperature distribution on coarse grids,and use local weighted regression method to smooth processing;use RBF neural networks to predict the temperature distribution on fine grids.Through numerical simulation with and without noise,compared with ART,SVD and Tikhonov,the proposed algorithm improves the reconstruction accuracy greatly and has the best anti-noise performance in the case of typical peak temperature.