Working Paper Article Version 1 This version is not peer-reviewed

A Deep Convolutional Network for Multi-Type Signal Detection in Spectrogram

Version 1 : Received: 11 May 2020 / Approved: 12 May 2020 / Online: 12 May 2020 (12:50:41 CEST)

How to cite: Li, W.; Wang, K.; You, L. A Deep Convolutional Network for Multi-Type Signal Detection in Spectrogram. Preprints 2020, 2020050215 Li, W.; Wang, K.; You, L. A Deep Convolutional Network for Multi-Type Signal Detection in Spectrogram. Preprints 2020, 2020050215

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.

Subject Areas

deep learning; signal detection; wideband spectrogram; centerline

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