AEROSPACE SHANGHAI, Volume. 41, Issue 6, 14(2024)
Individual Satellite Identification Method Based on Multimodal Features of Telemetry Signals
Figures & Tables(12)
Fig. 1. Framework of the satellite individual identification principle
Fig. 4. Basic structure of the convolutional layer and pooling layer
Fig. 7. Curves of individual identification ratios of different model satellites
Table 1.
Train parameter setting
Table 2.
Comparison of the average identification ratios under different CNN Block stack layers View tableView in ArticleTable 2.
Comparison of the average identification ratios under different CNN Block stack layers 网络层数 平均识别率/% 1层CNN Block
2层CNN Block
3层CNN Block
4层CNN Block
5层CNN Block
69.52
80.16
88.72
88.63
88.75
Table 3.
Comparison of the average identification ratios under different LSTM layers View tableView in ArticleTable 3.
Comparison of the average identification ratios under different LSTM layers 网络层数 平均识别率/% 无LSTM层
1层LSTM
2层LSTM
3层LSTM
88.72
90.37
92.68
92.35
Table 4.
Comparison of the average identification ratios with and without statistical features View tableView in ArticleTable 4.
Comparison of the average identification ratios with and without statistical features 统计特征是否引入（0/1） 平均识别率/% 0
1
86.06
92.68
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Qing LYU, Jinglong MENG, Shiyao LI, Yi ZHANG, Heshuai LIU. Individual Satellite Identification Method Based on Multimodal Features of Telemetry Signals[J]. AEROSPACE SHANGHAI, 2024, 41(6): 14
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Received: Feb. 6, 2024
Accepted: --
Published Online: Mar. 7, 2025
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