Yubin Fu , Xiaochuan Ma , Yu Liu , Xintong Wu , Tianhang Ji , Xingyuan Pei
{"title":"低秩稀疏分解混响抑制与自适应卡尔曼滤波相结合的多ping运动目标检测方法","authors":"Yubin Fu , Xiaochuan Ma , Yu Liu , Xintong Wu , Tianhang Ji , Xingyuan Pei","doi":"10.1016/j.dsp.2025.105602","DOIUrl":null,"url":null,"abstract":"<div><div>The traditional low rank sparsity decomposition (LRSD) can not separate the moving target from the sparse fluctuation reverberation and interference. Thus, the target is frequently masked by reverberation and interference. Therefore, the reverberation and interference suppression LRSD algorithm innovatively combined with the adaptive Kalman filtering and connected region (LRSD-CR-AMFKF) is proposed in this paper. The algorithm utilizes the connected region and adaptive Kalman filtering obtaining the continuous moving target trajectory and separates from the fluctuation reverberation and interference. Meanwhile, the adaptive Kalman filtering compensates for the measurement error caused by the interference, which improves the anti-interference ability of the algorithm and reduces the target estimation error. Finally, the LRSD-CR-AMFKF algorithm is validated by the experimental data. Compared with the conventional Kalman filtering and the LRSD, the sparse coefficient is improved, the reverberation and interference are separated, the estimation error is decreased, and a clean and precise target is obtained.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105602"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low rank sparsity decomposition reverberation suppression combined with adaptive Kalman filtering method for detecting multi-ping moving target\",\"authors\":\"Yubin Fu , Xiaochuan Ma , Yu Liu , Xintong Wu , Tianhang Ji , Xingyuan Pei\",\"doi\":\"10.1016/j.dsp.2025.105602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The traditional low rank sparsity decomposition (LRSD) can not separate the moving target from the sparse fluctuation reverberation and interference. Thus, the target is frequently masked by reverberation and interference. Therefore, the reverberation and interference suppression LRSD algorithm innovatively combined with the adaptive Kalman filtering and connected region (LRSD-CR-AMFKF) is proposed in this paper. The algorithm utilizes the connected region and adaptive Kalman filtering obtaining the continuous moving target trajectory and separates from the fluctuation reverberation and interference. Meanwhile, the adaptive Kalman filtering compensates for the measurement error caused by the interference, which improves the anti-interference ability of the algorithm and reduces the target estimation error. Finally, the LRSD-CR-AMFKF algorithm is validated by the experimental data. Compared with the conventional Kalman filtering and the LRSD, the sparse coefficient is improved, the reverberation and interference are separated, the estimation error is decreased, and a clean and precise target is obtained.</div></div>\",\"PeriodicalId\":51011,\"journal\":{\"name\":\"Digital Signal Processing\",\"volume\":\"168 \",\"pages\":\"Article 105602\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1051200425006244\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425006244","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Low rank sparsity decomposition reverberation suppression combined with adaptive Kalman filtering method for detecting multi-ping moving target
The traditional low rank sparsity decomposition (LRSD) can not separate the moving target from the sparse fluctuation reverberation and interference. Thus, the target is frequently masked by reverberation and interference. Therefore, the reverberation and interference suppression LRSD algorithm innovatively combined with the adaptive Kalman filtering and connected region (LRSD-CR-AMFKF) is proposed in this paper. The algorithm utilizes the connected region and adaptive Kalman filtering obtaining the continuous moving target trajectory and separates from the fluctuation reverberation and interference. Meanwhile, the adaptive Kalman filtering compensates for the measurement error caused by the interference, which improves the anti-interference ability of the algorithm and reduces the target estimation error. Finally, the LRSD-CR-AMFKF algorithm is validated by the experimental data. Compared with the conventional Kalman filtering and the LRSD, the sparse coefficient is improved, the reverberation and interference are separated, the estimation error is decreased, and a clean and precise target is obtained.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,