Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach
Author:
Affiliation:
1. University of Helsinki,Department of Computer Science,Finland
2. AI Analyst Immervision,Montreal,Canada
Funder
Academy of Finland
University of Helsinki
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9797013/9797014/09797026.pdf?arnumber=9797026
Reference30 articles.
1. A survey of transfer learning
2. A Comprehensive Survey on GNSS Interferences and the Application of Neural Networks for Anti-jamming
3. Jammer Classification in GNSS Bands Via Machine Learning Algorithms
4. GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
5. GNSS Cloud-Data Processing Technique for Jamming Detection and Localization
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Jammer Detection on Embedded Android Implementation: Blue Bird Group Case Study;2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI);2023-09-20
2. Possibilities of AI Algorithm Execution in GNSS;2022 URSI Regional Conference on Radio Science (USRI-RCRS);2022-12-01
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