{"title":"基于小波- resnet的组合导航故障检测方法","authors":"Yunhua Liu, Hao Wang, Yongqiang Liu, Qing Chang","doi":"10.1049/ell2.70316","DOIUrl":null,"url":null,"abstract":"<p>With the goal of realizing fault detection of airborne combined navigation system, this letter proposes a fault detection method combining wavelet transform and residual network. Firstly, the model equation of the system is established, and the combined navigation data set is constructed by combining the characteristics of the UAV flight trajectory. At the same time, the normal data of the UAV is added with common faults to obtain the fault data set. Then, the navigation data is preprocessed by discrete wavelet transform, and the residual network model is trained according to the preprocessed navigation data set to improve the training efficiency of the model. Finally, the trained model is tested and evaluated. The simulation results show that the accuracy of detecting mutation faults, slope faults and precision level reduction faults is more than <span></span><math>\n <semantics>\n <mrow>\n <mn>95</mn>\n <mo>%</mo>\n </mrow>\n <annotation>$95\\%$</annotation>\n </semantics></math>.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70316","citationCount":"0","resultStr":"{\"title\":\"A Combined Navigation Fault Detection Method Based on Wavelet-ResNet\",\"authors\":\"Yunhua Liu, Hao Wang, Yongqiang Liu, Qing Chang\",\"doi\":\"10.1049/ell2.70316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With the goal of realizing fault detection of airborne combined navigation system, this letter proposes a fault detection method combining wavelet transform and residual network. Firstly, the model equation of the system is established, and the combined navigation data set is constructed by combining the characteristics of the UAV flight trajectory. At the same time, the normal data of the UAV is added with common faults to obtain the fault data set. Then, the navigation data is preprocessed by discrete wavelet transform, and the residual network model is trained according to the preprocessed navigation data set to improve the training efficiency of the model. Finally, the trained model is tested and evaluated. The simulation results show that the accuracy of detecting mutation faults, slope faults and precision level reduction faults is more than <span></span><math>\\n <semantics>\\n <mrow>\\n <mn>95</mn>\\n <mo>%</mo>\\n </mrow>\\n <annotation>$95\\\\%$</annotation>\\n </semantics></math>.</p>\",\"PeriodicalId\":11556,\"journal\":{\"name\":\"Electronics Letters\",\"volume\":\"61 1\",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70316\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronics Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/ell2.70316\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ell2.70316","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}
A Combined Navigation Fault Detection Method Based on Wavelet-ResNet
With the goal of realizing fault detection of airborne combined navigation system, this letter proposes a fault detection method combining wavelet transform and residual network. Firstly, the model equation of the system is established, and the combined navigation data set is constructed by combining the characteristics of the UAV flight trajectory. At the same time, the normal data of the UAV is added with common faults to obtain the fault data set. Then, the navigation data is preprocessed by discrete wavelet transform, and the residual network model is trained according to the preprocessed navigation data set to improve the training efficiency of the model. Finally, the trained model is tested and evaluated. The simulation results show that the accuracy of detecting mutation faults, slope faults and precision level reduction faults is more than .
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
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