In the low orbit satellite Internet of things system, the spatial resolution of spectrum data perceived by satellites is low, and the details of the spatial electromagnetic environment are difficult to analyze. To solve this problem, this paper proposes to process the spatial distribution of the spectrum in the form of two-dimensional images, and to adopt the appropriate image super-resolution reconstruction algorithm according to the characteristics of spatial spectrum sensing data, so as to improve the spatial resolution of the spectrum, enhance the details in the spectrum situation. Simulation results show that the image signal of existence can be directly observed from the grey value. The bicubic interpolation method chosen according to spectral data characteristics, the Bayesian method based on L1 norm prior, and the learning method based on image blocks matching can effectively improves the spatial resolution of the spectrum data. When evaluating with PSNR, the reconstruction algorithm based on L1 norm prior is better. But, the learning method based on image blocks matching enhances the ripples in the spectrum sensing data. From a visual point of view, the effect of improving details is slightly better.
WEI Rui
,
XIE Zhuochen
,
LIU Jie
,
LIU Huijie
. Spatial resolution improvement of spectrum sensing data of LEO satellite based on image super-resolution[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(3)
: 386
-392
.
DOI: 10.7523/j.ucas.2020.0033
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