Version 1
: Received: 19 October 2024 / Approved: 21 October 2024 / Online: 21 October 2024 (11:54:11 CEST)
How to cite:
Hao, X.; Yang, S. VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition. Preprints2024, 2024101584. https://doi.org/10.20944/preprints202410.1584.v1
Hao, X.; Yang, S. VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition. Preprints 2024, 2024101584. https://doi.org/10.20944/preprints202410.1584.v1
Hao, X.; Yang, S. VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition. Preprints2024, 2024101584. https://doi.org/10.20944/preprints202410.1584.v1
APA Style
Hao, X., & Yang, S. (2024). VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition. Preprints. https://doi.org/10.20944/preprints202410.1584.v1
Chicago/Turabian Style
Hao, X. and Shuyuan Yang. 2024 "VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition" Preprints. https://doi.org/10.20944/preprints202410.1584.v1
Abstract
Wideband signal detection and identification are crucial for military and civilian fields such as communication reconnaissance and spectrum security. This paper systematically sorts out the mainstream technologies of wideband signal detection and identification, and expounds the main current short-wave wideband signal detection and identification work from three perspectives: traditional expert feature methods, combination of expert features and machine learning, and wideband signal detection and identification based on deep learning. Particularly, this paper focuses on the processing methods of current key signals such as short burst signals and frequency hopping signals, as well as the latest deep learning methods in the past five years. Finally, the shortcomings of the existing broadband signal detection and identification methods are summarized, and the future trend is predicted.
Keywords
wideband signal; detection; identification; deep learning; expert feature
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
