基于压缩感知的AT温度场重建算法OA北大核心CSTPCD
Reconstruction Algorithm of AT Temperature Field Based on Compressed Sensing
为提高声学层析成像(AT)的温度场重建能力,提出了基于压缩感知(CS)的AT温度场重建算法(CS-AT算法).算法利用信号稀疏性,减少待求数据量,降低逆问题求解难度.首先选择适当字典,构建基于CS的AT温度场重建正逆问题框架;然后以正交匹配追踪(OMP)算法进行CS重构,得到温度场重建待求量在稀疏域的解;最后将其变换回原始域,通过三次样条插值得到37 ×37像素的细致温度分布.通过数值仿真,分别以经典的最小二乘法(LSM)和CS-AT算法在有噪声和无噪声条件下重建了均温、单峰、双峰和四峰模型温度场;在自主研发的实验系统上重建了均温、单峰和双峰实际温度场.仿真和实验表明:CS-AT算法可有效降低温度场重建误差;四峰温度场下,CS-AT算法的最高重建误差最低,仅为LSM的25.5%.
To improve the temperature field reconstruction ability of acoustic tomography(AT),an AT temperature field reconstruction algorithm based on compressed sensing(CS-AT algorithm)was proposed.The algorithm utilizes signal sparsity to reduce the amount of data to be solved and reduce the difficulty of solving inverse problems.Firstly,selected an appropriate dictionary and constructed a framework based on CS for the forward and inverse problem of temperature field reconstruction by AT.Then,the orthogonal matching pursuit(OMP)algorithm was used for CS reconstruction to obtain the solution of the temperature field reconstruction in the sparse domain.Finally,transformed it back to the original domain and interpolated it to 37 × 37 pixel fine temperature distribution using cubic splines.Through numerical simulation,for kinds of models temperature fields(average temperature,single peak,bimodal,and four peak)were reconstructed using the classical least squares method(LSM)and CS-AT algorithm under noisy and non-noisy conditions respectively.Average temperature,single peak,and double peak actual temperature fields were reconstructed on an independently developed experimental system.Simulation and experiments have shown that CS-AT algorithm can effectively reduce temperature field reconstruction errors.Under the four peak temperature field,the highest reconstruction error of CS-AT algorithm is only 25.5%of LSM.
魏元焜;颜华;周英钢
沈阳工业大学信息科学与工程学院,辽宁沈阳 110870
温度测量声学层析成像温度场重建压缩感知正交匹配追踪
temperature measurementacoustic tomographytemperature field reconstructioncompressed sensingOMP
《计量学报》 2024 (005)
671-677 / 7
国家自然科学基金(61372154);辽宁省博士科研启动基金(201601157)
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