A Specific Emitter Identification Method Based on RF-DNA and XGBoost
Author:
Affiliation:
1. Aviation University of Air Force,Changchun,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9778230/9778231/09778627.pdf?arnumber=9778627
Reference11 articles.
1. Frequency-domain characterization of varying random vibration loading by a non-stationarity matrix;trapp;Int J Fatigue,2020
2. Specific emitter identification using geometric features of frequency drift curve;zhao;Bull Pol Acad Techn Sci,2018
3. XGBoost model for chronic kidney disease diagnosis;adeola;IEEE/ACM Transactions on Computational Biology and Bioinformatics,2020
4. ZigBee Device Verification for Securing Industrial Control and Building Automation Systems
5. A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost
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1. MRFE: A Deep-Learning-Based Multidimensional Radio Frequency Fingerprinting Enhancement Approach for IoT Device Identification;IEEE Internet of Things Journal;2024-09-15
2. Specific emitter identification by wavelet residual network based on attention mechanism;IET Communications;2024-06-24
3. Multi-resolution convolutional neural network for specific emitter identification;Proceedings of the 2024 6th International Symposium on Signal Processing Systems;2024-03-22
4. Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition;Sensors;2024-01-30
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