{"title":"基于多帧并行恢复方法的微动目标不完全雷达数据时频分析","authors":"Shichao Xiong;Hongwei Zhang;Kaiming Li;Ying Luo;Qun Zhang","doi":"10.1109/TIM.2025.3604106","DOIUrl":null,"url":null,"abstract":"Conventional time-frequency (TF) analysis methods deteriorate under the condition of data missing and low signal-to-noise ratio (SNR). Sparse signal processing (SSP) method is an effective solution for recovering TF images from incomplete data, but it is burdened by significant computational and storage requirements. To address these challenges, this study proposed a TF image recovery method called multiframe parallel recovery (MFPR) deep unfolded segmentation network. First, the TF recovery optimization problem solved by the proximal gradient (PG) method is constructed based on the MFPR signal model, which can simultaneously recover TF images from all frames. This can eliminate the need for vectorization and thereby reduce computational and storage costs. Then, the MFPR deep unfolded segmentation network (MDUS-Net), which contains two subnetworks, is proposed to obtain a complete TF image from incomplete data. The first subnetwork is a deep unfolded network of MFPR, which achieves high-quality TF recovery from incomplete data. The second subnetwork is a segmentation network, which achieves TF curves skeleton extraction via image segmentation technique. The experimental results on both simulated and measured data demonstrate that the proposed method can generate high-quality TF images even under conditions of incomplete data and low SNR.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time-Frequency Analysis for Incomplete Radar Data of Micromotion Targets via Multiframe Parallel Recovery Approach\",\"authors\":\"Shichao Xiong;Hongwei Zhang;Kaiming Li;Ying Luo;Qun Zhang\",\"doi\":\"10.1109/TIM.2025.3604106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conventional time-frequency (TF) analysis methods deteriorate under the condition of data missing and low signal-to-noise ratio (SNR). Sparse signal processing (SSP) method is an effective solution for recovering TF images from incomplete data, but it is burdened by significant computational and storage requirements. To address these challenges, this study proposed a TF image recovery method called multiframe parallel recovery (MFPR) deep unfolded segmentation network. First, the TF recovery optimization problem solved by the proximal gradient (PG) method is constructed based on the MFPR signal model, which can simultaneously recover TF images from all frames. This can eliminate the need for vectorization and thereby reduce computational and storage costs. Then, the MFPR deep unfolded segmentation network (MDUS-Net), which contains two subnetworks, is proposed to obtain a complete TF image from incomplete data. The first subnetwork is a deep unfolded network of MFPR, which achieves high-quality TF recovery from incomplete data. The second subnetwork is a segmentation network, which achieves TF curves skeleton extraction via image segmentation technique. The experimental results on both simulated and measured data demonstrate that the proposed method can generate high-quality TF images even under conditions of incomplete data and low SNR.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-14\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-08-29\",\"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/11145209/\",\"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/11145209/","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}
Time-Frequency Analysis for Incomplete Radar Data of Micromotion Targets via Multiframe Parallel Recovery Approach
Conventional time-frequency (TF) analysis methods deteriorate under the condition of data missing and low signal-to-noise ratio (SNR). Sparse signal processing (SSP) method is an effective solution for recovering TF images from incomplete data, but it is burdened by significant computational and storage requirements. To address these challenges, this study proposed a TF image recovery method called multiframe parallel recovery (MFPR) deep unfolded segmentation network. First, the TF recovery optimization problem solved by the proximal gradient (PG) method is constructed based on the MFPR signal model, which can simultaneously recover TF images from all frames. This can eliminate the need for vectorization and thereby reduce computational and storage costs. Then, the MFPR deep unfolded segmentation network (MDUS-Net), which contains two subnetworks, is proposed to obtain a complete TF image from incomplete data. The first subnetwork is a deep unfolded network of MFPR, which achieves high-quality TF recovery from incomplete data. The second subnetwork is a segmentation network, which achieves TF curves skeleton extraction via image segmentation technique. The experimental results on both simulated and measured data demonstrate that the proposed method can generate high-quality TF images even under conditions of incomplete data and low SNR.
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.