A single frame spectrum signal detection approach based on deep learning

Liu, Depeng; Xu, Jianxin; Cheng, Xiang; Yang, Mingyang

doi:10.1109/itoec49072.2020.9141675

Cited by 3 publications

(1 citation statement)

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“…The traditional HF signal detection and identification methods divide the above demand into two tasks: signal detection [3] and modulation identification [4]. Signal detection methods, including threshold-based methods, nonthreshold-based methods, and DL-based methods [5,6], usually perform in a narrow band to detect the presence of signals. Modulation identification methods mainly use feature-based (FB) methods.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Multisignal Detection and Identification in Known and Unknown HF Channels: A Deep Learning Method

Lin

Dai

2022

Wireless Communications and Mobile Computing

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The high-frequency (HF) signal detection and identification plays an import role in HF communications, and it is challenging since the HF environment randomly varies. Due to the success of deep learning (DL) methods in the fields of computer vision and natural language processing, some researchers adopt DL-based object detection methods to detect and identify signals in wideband spectrograms and achieve the good performance. However, the existing DL-based methods are not suitable for real-time HF signal detection, and their performance will be significantly degraded when these methods are applied to an unknown HF environment. In this paper, we design a novel multiresolution signal detection and identification network for real-time HF signal detection and identification and propose a domain adaptation method to adapt the network to unknown environments. The experimental results show that the running speed and accuracy of our designed network are superior to ones of the existing DL-based networks in different HF environments, and the proposed domain adaptation method can achieve obvious performance improvement in unknown environments.

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