{"title":"Multi-component signal separation based on ALSAE","authors":"","doi":"10.1007/s11276-024-03698-1","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Most research in the field of radar signal processing focuses on the use of time-frequency images (TFIs) to distinguish between different signal types. However, most studies have only examined the TFIs of a single signal, making it challenging to analyze and process the simultaneous reception of multiple signal components. This study proposes the use of adversarial latent separation auto encoder to separate and recognize multi-component signals, and innovatively propose a multi-network structure of feature extraction sub-network and signal separation sub-network. Thus, the problem of multi-component signal recognition is solved. Following separation, each component retains its time-frequency data while removing the influence of other components, and the separated TFIs are then subjected to parameter estimation and structural similarity (SSIM) measurements. The experimental findings demonstrate that the parameters retrieved from the separated signal have a low error with respect to the original signal, especially at low signal-to-noise ratios. The excellent SSIM and parameter estimation metrics between the separation results and the time-frequency image of the target tag imply that the separated single-component signal can be successfully reconstructed.</p>","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"120 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wireless Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11276-024-03698-1","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Most research in the field of radar signal processing focuses on the use of time-frequency images (TFIs) to distinguish between different signal types. However, most studies have only examined the TFIs of a single signal, making it challenging to analyze and process the simultaneous reception of multiple signal components. This study proposes the use of adversarial latent separation auto encoder to separate and recognize multi-component signals, and innovatively propose a multi-network structure of feature extraction sub-network and signal separation sub-network. Thus, the problem of multi-component signal recognition is solved. Following separation, each component retains its time-frequency data while removing the influence of other components, and the separated TFIs are then subjected to parameter estimation and structural similarity (SSIM) measurements. The experimental findings demonstrate that the parameters retrieved from the separated signal have a low error with respect to the original signal, especially at low signal-to-noise ratios. The excellent SSIM and parameter estimation metrics between the separation results and the time-frequency image of the target tag imply that the separated single-component signal can be successfully reconstructed.
期刊介绍：
The wireless communication revolution is bringing fundamental changes to data networking, telecommunication, and is making integrated networks a reality. By freeing the user from the cord, personal communications networks, wireless LAN''s, mobile radio networks and cellular systems, harbor the promise of fully distributed mobile computing and communications, any time, anywhere.
Focusing on the networking and user aspects of the field, Wireless Networks provides a global forum for archival value contributions documenting these fast growing areas of interest. The journal publishes refereed articles dealing with research, experience and management issues of wireless networks. Its aim is to allow the reader to benefit from experience, problems and solutions described.
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