[3] [3] ELTOM H,KANDEEPAN S,LIANG Y,et al. Cooperative soft fusion for HMM-based spectrum occupancy prediction[J].IEEE Communications Letters, 2018,22(10):2144-2147.

[4] [4] H?YHTY? M,POLLIN S,M?MMEL? A. Classification-based predictive channel selection for cognitive radios[C]// 2010 IEEE International Conference on Communications. Cape Town,South Africa:IEEE, 2010:1-6.

[5] [5] TALEBI B,DEHKORDI M N. Sensitive association rules hiding using electromagnetic field optimization algorithm[J].Expert Systems with Applications, 2018(114):155-172.

[8] [8] HOU Changbo,ZHANG Xiao,CHEN Xiang. Electromagnetic signal feature fusion and recognition based on multi-modal deep learning [J]. International Journal of Performability Engineering, 2020,16(6):941.

[9] [9] PRZYSTUPA Krzysztof,VASILKIVSKIJ Igor,ISHCHENKO Vitalii,et al. Electromagnetic pollution:case study of energy transmission lines and radio transmission equipment[J]. Przeglad Elektrotechniczny, 2020(2):52-55.

[10] [10] HABER E,SCHWARZBACH C. Parallel inversion of large-scale airborne time-domain electromagnetic data with multiple OcTree meshes[J]. Inverse Problems, 2014,30(5):055011.

[11] [11] ZHAO Ying,LUO Xiaobo,LIN Xiaoru,et al. Visual analytics for electromagnetic situation awareness in radio monitoring and management[J]. IEEE Transactions on Visualization and Computer Graphics, 2019,26(1):590-600.

[12] [12] CORREA N M,ADALI T,LI Y O,et al. Canonical correlation analysis for data fusion and group inferences[J]. IEEE Signal Processing Magazine, 2010,27(4):39-50.

[13] [13] DING Guoru,JIAO Yutao,WANG Jinlong,et al. Spectrum inference in cognitive radio networks:algorithms and applications[J].IEEE Communications Surveys & Tutorials, 2017,20(1):150-182.

[14] [14] RYABOV A,BAUM P. Electron microscopy of electromagnetic waveforms[J]. Science, 2016,353(6297):374-377.