浏览全部资源
扫码关注微信
1. 南京邮电大学物联网学院,江苏 南京 210003
2. 南京邮电大学通信与信息工程学院,江苏 南京 210003
3. 南京邮电大学卫星通信研究所,江苏 南京 210003
[ "吕秋霖(1996-),男,南京邮电大学物联网学院硕士生,主要研究方向为卫星频谱智能认知" ]
[ "丁晓进(1981-),男,南京邮电大学物联网学院副教授,主要研究方向为空间信息网络、卫星物联网、频谱智能认知等" ]
[ "张更新(1967-),男,南京邮电大学通信与信息工程学院教授,主要研究方向为空间信息网络、卫星通信等" ]
网络出版日期:2022-09,
纸质出版日期:2022-09-20
移动端阅览
吕秋霖, 丁晓进, 张更新. 卫星频谱信号智能挖掘技术[J]. 天地一体化信息网络, 2022,3(3):30-36.
Qiulin LYU, Xiaojin DING, Gengxin ZHANG. Intelligent Excavation Technologies for Satellite Spectrum Signals[J]. Space-integrated-ground information networks, 2022, 3(3): 30-36.
吕秋霖, 丁晓进, 张更新. 卫星频谱信号智能挖掘技术[J]. 天地一体化信息网络, 2022,3(3):30-36. DOI: 10.11959/j.issn.2096-8930.2022029.
Qiulin LYU, Xiaojin DING, Gengxin ZHANG. Intelligent Excavation Technologies for Satellite Spectrum Signals[J]. Space-integrated-ground information networks, 2022, 3(3): 30-36. DOI: 10.11959/j.issn.2096-8930.2022029.
随着空间信息网络的建设,可用频谱资源日益紧缺,面临的电磁环境越来越复杂,实现对全球电磁频谱态势的安全掌控所面临的挑战更加突出。针对以上问题,研究电磁频谱占用状态感知方法,通过构建时间卷积神经网络来感知频谱占用状态;探索频谱参数提取方法,结合底噪拟合和聚类分析,实现对中心频率、带宽、峰值功率等参数的提取;为挖掘异常频谱数据,研究利用神经网络自动检测起伏底噪、频谱草和大带宽等异常频谱;考虑星地间的大传播时延,进一步通过构建神经网络来实现对频谱占用状态的预测;将卫星频谱信号的感知、异常、参数和预测结果进行可视化呈现。初步评估结果表明,所设计的一套频谱感知、异常检测、参数认知和态势预测等卫星频谱智能挖掘技术,在提升频谱态势安全掌控能力的同时,还能有效提升频谱资源利用率。
With the construction of spatial information network
the available spectrum resources are increasingly scarce
and the electromagnetic environment is becoming more and more complex
resulting a more prominent challenge of realizing the safe control of the global electromagnetic.Therefore
the sensing method of electromagnetic spectrum occupation state was studied
a time convolution neural network to sense the spectrum occupation state was constructed.To mine abnormal spectrum data
neural network was used to automatically detect abnormal spectrum
such as fl uctuation noise fl oor
spectrum grass and large bandwidth.The spectrum parameter extraction method was explored.Considering the large propagation delay between the spectrum-sensing satellite and its gateway
a neural network was constructed to predicted the spectrum occupation state.The perception
anomaly
parameters and prediction results of satellite spectrum signal were visualized.The preliminary evaluation results showed that the designed satellite-spectrum intelligent mining technologies could not only improved the sensing ability of spectrum situation
but also eff ectively improved the utilization of spectrum resources.
OH H , NAM H . Energy detection scheme in the presence of burst signals [J ] . IEEE Signal Processing Letters , 2019 , 26 ( 4 ): 582 - 586 .
ZHANG X Z , GAO F F , CHAI R , et al . Matched filter based spectrum sensing when primary user has multiple power levels [J ] . China Communications , 2015 , 12 ( 2 ): 21 - 31 .
刘顺兰 , 王静 , 包建荣 . 高检测概率协方差矩阵机会协作频谱感知 [J ] . 电信科学 , 2019 , 35 ( 1 ): 67 - 73 .
LIU S L , WANG J , BAO J R . Covariance matrix opportunistic cooperative spectrum sensing of high detection probability [J ] . Telecommunications Science , 2019 , 35 ( 1 ): 67 - 73 .
燕展 , 康凯 , 王红军 . 一种改进的卫星MPSK通信信号盲载频估计算法 [J ] . 电讯技术 , 2013 , 53 ( 9 ): 1186 - 1190 .
YAN Z , KANG K , WANG H J . An improved blind carrier frequency estimation algorithm for satellite MPSK signals [J ] . Telecommunication Engineering , 2013 , 53 ( 9 ): 1186 - 1190 .
TANG H , LIU H , XIAO W , et al . When dictionary learning meets deep learning,deep dictionary learning and coding network for image recognition with limited data [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 32 ( 5 ): 2129 - 2141 .
ZHANG L , ZHAO M , TAN C , et al . Research on spectrum sensing system based on composite neural network [C ] // Proceedings of 2020 2nd International Conference on Advances in Computer Technology,Information Science and Communications (CTISC) . Piscataway,IEEE Press , 2020 : 22 - 26 .
徐达 , 徐湘莹 , 王世祺 . 频谱感知技术在无线电中运用的探讨 [J ] . 中国信息化 , 2021 ( 11 ): 56 - 57 .
XU D , XU X Y , WANG S Q . Discussion on application of spectrum sensing technology in radio [J ] . Informatization in China , 2021 ( 11 ): 56 - 57 .
SARIKHANI R , KEYNIA F . Cooperative spectrum sensing meets machine learning,deep reinforcement learning approach [J ] . IEEE Communications Letters , 2020 , 24 ( 7 ): 1459 - 1462 .
NI T , DING X J , WANG Y F , et al . Spectrum sensing via temporal convolutional network [J ] . China Communications , 2021 , 18 ( 9 ): 37 - 47 .
WANG Q C , WANG L J . An improved subband peak energy detection method [C ] // Proceedings of 2016 IEEE/OES China Ocean Acoustics (COA) . Piscataway,IEEE Press , 2016 : 1 - 5 .
NI X , WANG H L , YANG Y , et al . Polyphase-modulated radar signal recognition based on time-frequency amplitude and phase features [C ] // Proceedings of 2020 13th International Congress on Image and Signal Processing,BioMedical Engineering and Informatics (CISP-BMEI) . Piscataway,IEEE Press , 2020 : 552 - 556 .
GAO L , QIN P , LI H , et al . Transfer learninng in polytime codes signal recognition [C ] // Proceedings of 2019 IEEE 4th International Conference on Signal and Image Processing . Piscataway,IEEE Press , 2019 : 91 - 95 .
朱文登 . 基于计算机视觉的卫星频谱信号识别研究 [D ] . 南京,南京邮电大学 , 2020 .
ZHU W D . Research on satellite spectrum signal recognition based on computer vision [D ] . Nanjing,Nanjing University of Posts and Telecommunications , 2020 .
REDMON J , FARHADI A . YOLO9000,better,faster,stronger [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway,IEEE Press , 2017 : 6517 - 6525 .
WANG X Y , PENG T , ZUO P L , et al . Spectrum prediction method for ISM bands based on LSTM [C ] // Proceedings of 2020 5th International Conference on Computer and Communication Systems (ICCCS) . Piscataway,IEEE Press , 2020 : 580 - 584 .
DING X J , FENG L J , ZOU Y L , et al . Deep learning aided spectrum prediction for satellite communication systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 12 ): 16314 - 16319 .
0
浏览量
828
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构