{"title":"基于贝叶斯压缩感知的雷达信号自适应测量","authors":"Wei Wang, Baoju Zhang","doi":"10.1109/ICC.2012.6364765","DOIUrl":null,"url":null,"abstract":"The theory of Bayesian Compressive Sensing is briefly introduced. An evaluation index based on differential entropy of estimated signal is devised and the adaptive compressive measurement procedure without any prior information about the measured signals is presented in block manner. Numerical simulations on random step signal and real radar signal verify that the adaptive algorithm has good performance. This novel offers great potential for adaptive compressive measuring real time signal.","PeriodicalId":331080,"journal":{"name":"2012 IEEE International Conference on Communications (ICC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian compressive sensing for adaptive measurement of radar signal\",\"authors\":\"Wei Wang, Baoju Zhang\",\"doi\":\"10.1109/ICC.2012.6364765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The theory of Bayesian Compressive Sensing is briefly introduced. An evaluation index based on differential entropy of estimated signal is devised and the adaptive compressive measurement procedure without any prior information about the measured signals is presented in block manner. Numerical simulations on random step signal and real radar signal verify that the adaptive algorithm has good performance. This novel offers great potential for adaptive compressive measuring real time signal.\",\"PeriodicalId\":331080,\"journal\":{\"name\":\"2012 IEEE International Conference on Communications (ICC)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Communications (ICC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICC.2012.6364765\",\"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 International Conference on Communications (ICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICC.2012.6364765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian compressive sensing for adaptive measurement of radar signal
The theory of Bayesian Compressive Sensing is briefly introduced. An evaluation index based on differential entropy of estimated signal is devised and the adaptive compressive measurement procedure without any prior information about the measured signals is presented in block manner. Numerical simulations on random step signal and real radar signal verify that the adaptive algorithm has good performance. This novel offers great potential for adaptive compressive measuring real time signal.
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