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《Opto-Electronic Engineering》 2018-02
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Sparse decomposition of atmospheric turbulence wavefront gradient

Li Juanjuan;Cai Dongmei;Jia Peng;Li Can;Institute of Physics and Optoelectronics, Taiyuan University of Technology;Institute of Information Engineering, Taiyuan University of Technology;  
Using compressive sensing technology in atmospheric turbulent wavefront detected data compression can greatly reduce the amount of measured data, can effectively reduce the pressure of data transmission and storage, which is good for real-time measurement of turbulent wavefront. However, the wavefront signal is required to be sparse or can be sparsely represented in one transform domain. In this paper, a preliminary study of the sparsity of the atmospheric turbulent wavefront gradient signal is carried out. Based on the statistical characteristics of atmospheric turbulence, the golden section(GS) is used to make the turbulent power spectrum in the frequency domain, and the sparse basis is established to meet the physical characteristics of the turbulent gradient, then the sparsity of the gradient of the turbulent wavefront is clarified. The sparse decomposition of the wavefront gradient is simulated by using the GS sparse base, and the sparse decomposition effect of different sparsity bases on the wavefront gradient is compared. On this basis, using the GS basis as the initialization training dictionary, K singular value decomposition(KSVD) dictionary training is carried out to get the training base(KSVD-GS), and then thesparse representation performance of this training base to the wavefront gradient signal is analyzed. This paper verifies that the wavefront gradient can be sparsely decomposed and build a better sparse basis, and provides the precondition for the application of compressive sensing.
【Fund】: 国家自然科学基金青年科学基金项目(11503018);; 天文联合基金项目(U1631133)~~
【CateGory Index】: O357.5
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