You are currently viewing a beta version of our website. If you spot anything unusual, kindly let us know.
Preprint
Article
A Deep Convolutional Network for Multi-Type Signal Detection in Spectrogram
Altmetrics
Downloads
467
Views
324
Comments
0
This version is not peer-reviewed
Abstract
Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless technology, and the effect is obvious. In this paper, we propose a novel neural network for multi-type signal detection that can locate signals and recognize signal types in wideband spectrogram. Our network utilizes the key point estimation to locate the rough centerline of signal region and identify class. Then, several regressions are carried out to achieve properties, such as local offset and border offsets of bounding box, which is further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same detection task. Specifically, our method runs obviously faster than existing methods, and abandons the anchor generation, which makes it more favorable for real-time applications.
Keywords:
Subject: Engineering - Control and Systems Engineering
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Submitted:
11 May 2020
Posted:
12 May 2020
You are already at the latest version
Alerts
This version is not peer-reviewed
Submitted:
11 May 2020
Posted:
12 May 2020
You are already at the latest version
Alerts
Abstract
Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless technology, and the effect is obvious. In this paper, we propose a novel neural network for multi-type signal detection that can locate signals and recognize signal types in wideband spectrogram. Our network utilizes the key point estimation to locate the rough centerline of signal region and identify class. Then, several regressions are carried out to achieve properties, such as local offset and border offsets of bounding box, which is further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same detection task. Specifically, our method runs obviously faster than existing methods, and abandons the anchor generation, which makes it more favorable for real-time applications.
Keywords:
Subject: Engineering - Control and Systems Engineering
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation
Meiyan Lin
et al.
Sensors,
2022
FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection
Hao Huang
et al.
Electronics,
2022
Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
Shirong Guo
et al.
Sensors,
2023
MDPI Initiatives
Important Links
© 2024 MDPI (Basel, Switzerland) unless otherwise stated