Abstract：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.
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2024, Vol. 45 
