Xiuli Xin;Xinran Chen;Hongyu Zhou;Xiaoxue Feng;Weixing Li;Zhenxu Li;Feng Pan
{"title":"基于高斯混合滤波的残差神经网络联合误差辨识与目标定位","authors":"Xiuli Xin;Xinran Chen;Hongyu Zhou;Xiaoxue Feng;Weixing Li;Zhenxu Li;Feng Pan","doi":"10.1109/TIM.2025.3604958","DOIUrl":null,"url":null,"abstract":"Target localization is a key technology for unmanned aerial vehicle (UAV) applications in various fields, such as target tracking and task planning. However, the accuracy of UAV localization is significantly affected by systematic and random errors in attitude data, and the nonlinearity of the measurement model, together with the unknown distribution of measurement noise. To achieve robust and precise localization in long-distance oblique scenarios based on dynamic platforms, this article proposes a Gaussian Mixture Filter-incorporated self-attention (SA) Residual Neural Network (GMAR) algorithm for target localization. Firstly, an end-to-end SA residual neural network (SA-ResNN) model is built to accurately model both systematic and random errors in attitude angle. The SA mechanism is innovatively introduced to enhance the global feature representation capability of the residual module. Then, the Gaussian mixture (GM) filter utilizes a GM model to model the prior and posterior probability density functions, which can effectively capture the uncertainty in the state probability density function under nonlinear measurement models and enhance the robustness of the localization system. Finally, simulations and flight experiments demonstrate that the proposed GMAR algorithm can significantly improve the localization accuracy and robustness of ground targets in long-distance oblique scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gaussian Mixture Filter-Incorporated Self-Attention Residual Neural Network for UAV Joint Error Identification and Target Localization\",\"authors\":\"Xiuli Xin;Xinran Chen;Hongyu Zhou;Xiaoxue Feng;Weixing Li;Zhenxu Li;Feng Pan\",\"doi\":\"10.1109/TIM.2025.3604958\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Target localization is a key technology for unmanned aerial vehicle (UAV) applications in various fields, such as target tracking and task planning. However, the accuracy of UAV localization is significantly affected by systematic and random errors in attitude data, and the nonlinearity of the measurement model, together with the unknown distribution of measurement noise. To achieve robust and precise localization in long-distance oblique scenarios based on dynamic platforms, this article proposes a Gaussian Mixture Filter-incorporated self-attention (SA) Residual Neural Network (GMAR) algorithm for target localization. Firstly, an end-to-end SA residual neural network (SA-ResNN) model is built to accurately model both systematic and random errors in attitude angle. The SA mechanism is innovatively introduced to enhance the global feature representation capability of the residual module. Then, the Gaussian mixture (GM) filter utilizes a GM model to model the prior and posterior probability density functions, which can effectively capture the uncertainty in the state probability density function under nonlinear measurement models and enhance the robustness of the localization system. Finally, simulations and flight experiments demonstrate that the proposed GMAR algorithm can significantly improve the localization accuracy and robustness of ground targets in long-distance oblique scenarios.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-14\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11146878/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11146878/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Gaussian Mixture Filter-Incorporated Self-Attention Residual Neural Network for UAV Joint Error Identification and Target Localization
Target localization is a key technology for unmanned aerial vehicle (UAV) applications in various fields, such as target tracking and task planning. However, the accuracy of UAV localization is significantly affected by systematic and random errors in attitude data, and the nonlinearity of the measurement model, together with the unknown distribution of measurement noise. To achieve robust and precise localization in long-distance oblique scenarios based on dynamic platforms, this article proposes a Gaussian Mixture Filter-incorporated self-attention (SA) Residual Neural Network (GMAR) algorithm for target localization. Firstly, an end-to-end SA residual neural network (SA-ResNN) model is built to accurately model both systematic and random errors in attitude angle. The SA mechanism is innovatively introduced to enhance the global feature representation capability of the residual module. Then, the Gaussian mixture (GM) filter utilizes a GM model to model the prior and posterior probability density functions, which can effectively capture the uncertainty in the state probability density function under nonlinear measurement models and enhance the robustness of the localization system. Finally, simulations and flight experiments demonstrate that the proposed GMAR algorithm can significantly improve the localization accuracy and robustness of ground targets in long-distance oblique scenarios.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.