In this paper, a classification method based on convolutional neural network (CNN) and received signal strength (RSS) is proposed to solve the problem of non-cooperative emitter beam state sensing in electrical situational awareness. RSS, sensor coordinates, and received signal frequency are taken as the input features of CNN, while real state is taken as the output of CNN. To increase the RSS gradient contained in the eigenvector, a multi-layer sensor array is proposed to measure RSS. Simulation results show that the proposed method is robust to array location disturbance, and has the ability to generalize the mismatches in target location and main lobe beam width between first nulls.
蒋伊琳1
,
2，李向1
,
2，张昊平3
. Emitter Beam State Sensing Based on Convolutional Neural Network and Received Signal Strength[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(6)
: 1017
-1022
.
DOI: 10.1007/s12204-023-2582-2
[1] QIN Z J, GAO Y, PLUMBLEY M D, et al. Wideband spectrum sensing on real-time signals at subNyquist sampling rates in single and cooperative multiple nodes [J]. IEEE Transactions on Signal Processing,2016, 64(12): 3106-3117.
[2] MEHANNA O, SIDIROPOULOS N D. Maximum likelihood passive and active sensing of wideband power spectra from few bits [J]. IEEE Transactions on Signal Processing, 2015, 63(6): 1391-1403.
[3] YU Z, WANG Y, CHEN C. Radar emitter signal sorting method based on density clustering algorithm of signal aliasing degree judgment[C]// 2020 15th IEEE Conference on Industrial Electronics and Applications.Kristiansand: IEEE, 2020: 1027-1031.
[4] PAN Z S, WANG S F, ZHU M T, et al. Automatic waveform recognition of overlapping LPI radar signals based on multi-instance multi-label learning [J]. IEEE Signal Processing Letters, 2020, 27: 1275-1279.
[5] AZPURUA M A, POUS M, SILVA F. Decomposition of electromagnetic interferences in the time-domain [J].IEEE Transactions on Electromagnetic Compatibility,2016, 58(2): 385-392.
[6] LI P, ZHOU Z Y, SHENG M J. Identification of electromagnetic interferences based on adaptive sparsest time-frequency analysis [C]//2018 IEEE International Symposium on Electromagnetic Compatibility and 2018 IEEE Asia-Pacific Symposium on Electromagnetic Compatibility. Suntec City: IEEE, 2018: 761-
765.
[7] HE R M, XU Z F, WANG L H, et al. Research on 3D visualization technology of electromagnetic field based on OSG [C]//2017 10th International Symposium on Computational Intelligence and Design.Hangzhou: IEEE, 2017: 75-78.
[8] TITOV E V, SOSHNIKOV A A, DROBYAZKO ON. Experimental research of electromagnetic environment in domestic environment with computer visualization of electromagnetic pollution[C]// 2020 International Conference on Industrial Engineering, Applications and Manufacturing. Sochi: IEEE, 2020: 1-5.
[9] WANG G, CHEN H, LI Y M, et al. On receivedsignal-strength based localization with unknown transmit power and path loss exponent [J]. IEEE Wireless Communications Letters, 2012, 1(5): 536-539.
