基于稀疏重构的声学CT温度场重建  

Acoustic CT Temperature Field Reconstruction Based on Sparse Reconstruction

作　　者：魏元焜 颜华[1] 周英钢[1] WEI Yuan-kun;YAN Hua;ZHOU Ying-gang(School of Information Science&Engineering,Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]沈阳工业大学信息科学与工程学院,辽宁沈阳110870

出　　处：《仪表技术与传感器》2023年第6期88-93,共6页Instrument Technique and Sensor

基　　金：国家自然科学基金项目(61372154);辽宁省博士科研启动基金项目(201601157)。

摘　　要：为降低声学CT温度场重建误差,提出了一种基于稀疏重构的温度场重建算法。在建立基于稀疏重构的声学CT数学模型的基础上,算法设计了一种可提高稀疏重构精度的声学CT观测矩阵优化方法,以稀疏重构算法重建温度场,为稀疏重构算法设计了与噪声水平相关的迭代终止条件。温度场重建实验表明:提出的算法比经典的最小二乘法、SIRT算法重建误差更低,重建的温度场更接近于真实分布,对实际温度场重建有一定应用价值。In order to reduce the reconstruction error of acoustic CT temperature field,a temperature field reconstruction algorithm based on sparse reconstruction was proposed.On the basis of establishing the mathematical model of acoustic CT based on sparse reconstruction,the algorithm designed an optimization method of acoustic CT measurement matrix that can improve the accuracy of sparse reconstruction.The sparse reconstruction algorithm was used to reconstruct the temperature field,and the iteration stop conditions related to the noise level was designed for the sparse reconstruction algorithm.Temperature field reconstruction experiments show that the proposed algorithm has lower reconstruction error than the classical least square method and SIRT algorithm,and the reconstructed temperature field is closer to the real distribution,which has certain application value for reconstructing the actual temperature field.

关 键 词：温度场重建 声学层析成像 观测矩阵优化 稀疏重构 正交匹配追踪(OMP) 遗传算法 

分 类 号：TK31[动力工程及工程热物理—热能工程]