Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation
This paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adapti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/12/1815 |
| _version_ | 1797488071600504832 |
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| author | Changce Wang Fangpei Zhang Wenjiang Ouyang Xiaojun Jing Junsheng Mu |
| author_facet | Changce Wang Fangpei Zhang Wenjiang Ouyang Xiaojun Jing Junsheng Mu |
| author_sort | Changce Wang |
| collection | DOAJ |
| description | This paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adaptive manner. In order to estimate the SNR in the environment, not only is a convolutional neural network (CNN) applied in the proposed signal detection strategy, but a long shor-term memory network (LSTM) network is also included; in terms of features, it combines deep features and time-dimension features. The numbers of layers of the CNN and LSTM impact the performance of the algorithm. The decision on the presence or absence of a control signal is made at the fusion center (FC) based on the majority voting rule. This paper shows that the network with a two-layer CNN and a two-layer LSTM can achieve high estimation accuracy of environmental SNR. Simultaneously, the detection accuracy is improved by about 1 dB compared with the classical multichannel detection schemes. |
| first_indexed | 2024-03-09T23:56:54Z |
| format | Article |
| id | doaj.art-dc1403a6e6984c89a5aac35707b51fac |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T23:56:54Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-dc1403a6e6984c89a5aac35707b51fac2023-11-23T16:24:07ZengMDPI AGElectronics2079-92922022-06-011112181510.3390/electronics11121815Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR EstimationChangce Wang0Fangpei Zhang1Wenjiang Ouyang2Xiaojun Jing3Junsheng Mu4School of Automation and Electeical Engineering, University of Science and Technology, Beijing 100083, ChinaInformation Science Academy of China Electronics Technology Group Corporation, Beijing 100846, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaThis paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adaptive manner. In order to estimate the SNR in the environment, not only is a convolutional neural network (CNN) applied in the proposed signal detection strategy, but a long shor-term memory network (LSTM) network is also included; in terms of features, it combines deep features and time-dimension features. The numbers of layers of the CNN and LSTM impact the performance of the algorithm. The decision on the presence or absence of a control signal is made at the fusion center (FC) based on the majority voting rule. This paper shows that the network with a two-layer CNN and a two-layer LSTM can achieve high estimation accuracy of environmental SNR. Simultaneously, the detection accuracy is improved by about 1 dB compared with the classical multichannel detection schemes.https://www.mdpi.com/2079-9292/11/12/1815UAV detectionEDmajority votingSNR estimation |
| spellingShingle | Changce Wang Fangpei Zhang Wenjiang Ouyang Xiaojun Jing Junsheng Mu Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation Electronics UAV detection ED majority voting SNR estimation |
| title | Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation |
| title_full | Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation |
| title_fullStr | Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation |
| title_full_unstemmed | Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation |
| title_short | Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation |
| title_sort | non cooperative uav detection with semi adaptive sampling of control signal and snr estimation |
| topic | UAV detection ED majority voting SNR estimation |
| url | https://www.mdpi.com/2079-9292/11/12/1815 |
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