{"title":"Golay满足Hadamard:用于快速压缩感知的Golay配对Hadamard矩阵","authors":"Lu Gan, Kezhi Li, Cong Ling","doi":"10.1109/ITW.2012.6404755","DOIUrl":null,"url":null,"abstract":"This paper introduces Golay-paired Hadamard matrices for fast compressed sensing of sparse signals in the time or spectral domain. These sampling operators feature low-memory requirement, hardware-friendly implementation and fast computation in reconstruction. We show that they require a nearly optimal number of measurements for faithful reconstruction of a sparse signal in the time or frequency domain. Simulation results demonstrate that the proposed sensing matrices offer a reconstruction performance similar to that of fully random matrices.","PeriodicalId":325771,"journal":{"name":"2012 IEEE Information Theory Workshop","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Golay meets Hadamard: Golay-paired Hadamard matrices for fast compressed sensing\",\"authors\":\"Lu Gan, Kezhi Li, Cong Ling\",\"doi\":\"10.1109/ITW.2012.6404755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces Golay-paired Hadamard matrices for fast compressed sensing of sparse signals in the time or spectral domain. These sampling operators feature low-memory requirement, hardware-friendly implementation and fast computation in reconstruction. We show that they require a nearly optimal number of measurements for faithful reconstruction of a sparse signal in the time or frequency domain. Simulation results demonstrate that the proposed sensing matrices offer a reconstruction performance similar to that of fully random matrices.\",\"PeriodicalId\":325771,\"journal\":{\"name\":\"2012 IEEE Information Theory Workshop\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Information Theory Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITW.2012.6404755\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Information Theory Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITW.2012.6404755","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Golay meets Hadamard: Golay-paired Hadamard matrices for fast compressed sensing
This paper introduces Golay-paired Hadamard matrices for fast compressed sensing of sparse signals in the time or spectral domain. These sampling operators feature low-memory requirement, hardware-friendly implementation and fast computation in reconstruction. We show that they require a nearly optimal number of measurements for faithful reconstruction of a sparse signal in the time or frequency domain. Simulation results demonstrate that the proposed sensing matrices offer a reconstruction performance similar to that of fully random matrices.
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