W. Zhang, Kai Zeng, Chuan Zhu, Xingyue Long, Wei Yi
{"title":"A Multi-Frame Joint Tracking and Classification Method for Weak Target in Radar System","authors":"W. Zhang, Kai Zeng, Chuan Zhu, Xingyue Long, Wei Yi","doi":"10.1109/ICCAIS56082.2022.9990283","DOIUrl":null,"url":null,"abstract":"This paper solves the problem of joint tracking and classification of weak targets using multi-frame joint processing technology. Weak targets are easily submerged in background noise, and single-frame threshold detection makes many of their feature information lost, making it challenging to track and classify them effectively. Aiming at solving these problems, a multi-frame joint tracking and classification (MF-JTC) method is proposed. The method achieves the accurate estimation of the motion trajectory and target class by jointly processing the radar measurement data in multi-frames. Finally, the results show that compared with the traditional single-frame joint tracking and classification (SF-JTC) method, the proposed method has better tracking and classification performance for weak targets.","PeriodicalId":273404,"journal":{"name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS56082.2022.9990283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper solves the problem of joint tracking and classification of weak targets using multi-frame joint processing technology. Weak targets are easily submerged in background noise, and single-frame threshold detection makes many of their feature information lost, making it challenging to track and classify them effectively. Aiming at solving these problems, a multi-frame joint tracking and classification (MF-JTC) method is proposed. The method achieves the accurate estimation of the motion trajectory and target class by jointly processing the radar measurement data in multi-frames. Finally, the results show that compared with the traditional single-frame joint tracking and classification (SF-JTC) method, the proposed method has better tracking and classification performance for weak targets.