A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram
Joint Authors
Li, Weihao
Wang, Keren
You, Ling
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-12
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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 communication and have shown great potential.
In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms.
Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class.
Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are 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 task.
In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.
American Psychological Association (APA)
Li, Weihao& Wang, Keren& You, Ling. 2020. A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1202478
Modern Language Association (MLA)
Li, Weihao…[et al.]. A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram. Mathematical Problems in Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1202478
American Medical Association (AMA)
Li, Weihao& Wang, Keren& You, Ling. A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1202478
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202478