Spatial resolution improvement of spectrum sensing data of LEO satellite based on image super-resolution

doi:10.7523/j.ucas.2020.0033

Abstract

Abstract: 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.

Key words: low orbit satellite, spectrum sensing data, images super-resolution reconstruction, bicubic interpolation

CLC Number:

TN911.7

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.

References

[1] 杨健,陈曦,丁国如,等.基于区块链的频谱设备网络中防御拜占庭攻击的分布式共识机制[J].通信学报, 2020, 41(3): 1-16. DOI:10.11959/j.issn.1000-436x.2020044.
[2] 李竟铭.面向频谱地图构建的频谱态势生成技术研究 [D].南京:南京航空航天大学, 2019.
[3] 吴启晖,任敬.电磁频谱空间认知新范式:频谱态势[J].南京航空航天大学学报, 2016, 48(5): 625-632. DOI:10.16356/j.1005-2615.2016.05.002.
[4] 王运峰,丁晓进,张更新.GEO与LEO双层网络协同频谱感知研究[J].无线电通信技术, 2019,45 (6): 627-632. DOI:10.3969/j.issn.1003-3114.2019.06.010.
[5] Keys R G. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(6): 1153-1160. DOI:10.1109/TASSP.1981.1163711.
[6] Villena S, Vega M, Molina R, et al. Bayesian super resolution image reconstruction using an ℓ₁ prior [C] // 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, Seprember 16-18, 2009, Salzburg, Austria. IEEE, 2009: 152-157. DOI:10.1109/ISPA.2009.5297740.
[7] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars [C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. IEEE, 2015: 5197-5206. DOI:10.1109/CVPR.2015.7299156.
[8] 何国金.频谱态势三维可视化技术研究[J].电子制作, 2013(7): 36-37. DOI:10.16589/j.cnki.cn11-3571/tn.2013.07.004.
[9] Gonzalez R C, Woods R E.数字图像处理[M]. 3版. 阮秋琦, 阮宇志译. 北京:电子出版社,2011:31-38.
[10] Kim K, Xin Y, Rangarajan S. Energy detection based spectrum sensing for cognitive radio: an experimental study [C]//2010 IEEE Global Telecommunications Conference GLOBECOM 2010. December 6-10, 2010, Miami, FL, USA. IEEE, 2010: 1-5. DOI:10.1109/GLOCOM.2010.5683560.
[11] Sofotasios P C, Rebeiz E, Zhang L, et al. Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels[J]. IEEE Transactions on Vehicular Technology, 2013, 62(3):1031-1040. DOI:10.1109/TVT.2012.2228680.
[12] Gismalla E H, Alsusa E. A generalized system model and performance analysis for the periodogram-based energy detector [C]//2011 IEEE Global Telecommunications Conference-GLOBECOM 2011. December 5-9, 2011. Houston, TX, USA. IEEE, 2011: 1-5. DOI:10.1109/GLOCOM.2011.6134146.
[13] Alsusa E, Gismalla E H. An accurate model for periodogram-based energy detection over Nakagami fading [ C ]// 2012 IEEE International Conference on Communications (ICC). June 10-15, 2012, Ottawa, ON, Canada. IEEE, 2012: 1614-1618. DOI:10.1109/ICC.2012.6364019.
[14] 李欣,崔子冠,朱秀昌.超分辨率重建算法综述[J].电视技术, 2016, 40(9):1-9. DOI:10.16280/j.videoe.2016.09.001.
[15] Liu X, Guo T D, Han C Y, et al. Super resolution reconstruction of single image with denoising and upscaling[J]. Journal of University of Chinese Academy of Sciences, 2016, 33(5): 596-603. DOI:10.7523/j.issn.2095-6134.2016.05.004.
[16] 何保畅,耿修瑞,胡文龙.一种基于视觉特性和灰度补偿改进的LDR图像增强方法[J].中国科学院大学学报,2018,35(3):391-401. DOI:10.7523/j.issn.2095-6134.2018.03.014.
[17] Yang W M, Zhang X C, Tian Y P, et al. Deep learning for single image super-resolution: a brief review[J]. IEEE Transactions on Multimedia, 2019, 21(12): 3106-3121. DOI:10.1109/TMM.2019.2919431.
[18] Barnes C, Shechtman E, Finkelstein A, et al. PatchMatch: a randomized correspondence algorithm for structural image editing[J].ACM Transactions on Graphics, 2009, 28(3): article24. DOI:10.1145/1531326.1531330.
[19] Irani M, Peleg S. Improving resolution by image registration[J].CVGIP: Graphical Models and Image Processing, 1991, 53(3): 231-239. DOI:10.1016/1049-9652(91)90045-L.