{"title":"A fast direct locator for radiation source based on composite convolution neural network","authors":"Chenhao Gong, Guomei Zhang, Guobing Li, Yue Mao","doi":"10.1049/ell2.13271","DOIUrl":null,"url":null,"abstract":"<p>The high spatial search complexity of the direct positioning method in passive positioning systems leads to long positioning time and high computational resource consumption. In response to this issue, this article proposes a fast localization scheme based on composite convolutional neural networks (CCNN), which can effectively explore the correlation between the position of the radiation source and the characteristics of the received signal. CCNN is a 20-layer composite network based on fully convolutional network layer, which is composed of convolutional layers, batch normalization (BN) layers, and ReLU activation function layers with unidirectional connections. Then, CCNNs are adjusted and trained for positioning single and multiple radiation sources, respectively. Simulation results show that the computational time of the proposed method can be reduced by nearly 98% compared with the direct positioning scheme. Meanwhile, about 71.2% of positioning error's reduction is achieved.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.13271","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ell2.13271","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The high spatial search complexity of the direct positioning method in passive positioning systems leads to long positioning time and high computational resource consumption. In response to this issue, this article proposes a fast localization scheme based on composite convolutional neural networks (CCNN), which can effectively explore the correlation between the position of the radiation source and the characteristics of the received signal. CCNN is a 20-layer composite network based on fully convolutional network layer, which is composed of convolutional layers, batch normalization (BN) layers, and ReLU activation function layers with unidirectional connections. Then, CCNNs are adjusted and trained for positioning single and multiple radiation sources, respectively. Simulation results show that the computational time of the proposed method can be reduced by nearly 98% compared with the direct positioning scheme. Meanwhile, about 71.2% of positioning error's reduction is achieved.
期刊介绍:
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO