{"title":"基于顺序压缩感知的车联网目标定位算法","authors":"Xiuqin Li, Tianjing Wang, Guangwei Bai, Xinjie Guan","doi":"10.1109/ISC2.2018.8656987","DOIUrl":null,"url":null,"abstract":"The grid-based target localization for internet of vehicle has the property of sparsity, which can be transformed to a sparse recovery problem due to compressive sensing. The accurate target localization relies on the sufficient measurements, but we cannot determine the number of measurements without knowing the number of targets. In this poster, we propose a novel target localization algorithm based on sequential compressed sensing to select the optimal number of measurements and estimate target locations via lp-norm (0<p<1) minimization. The experimental results show that our proposed algorithm has better localization performance than localization via l0 -norm or l1 -norm minimization.","PeriodicalId":344652,"journal":{"name":"2018 IEEE International Smart Cities Conference (ISC2)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Target Localization Algorithm Based on Sequential Compressed Sensing for Internet of Vehicles\",\"authors\":\"Xiuqin Li, Tianjing Wang, Guangwei Bai, Xinjie Guan\",\"doi\":\"10.1109/ISC2.2018.8656987\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The grid-based target localization for internet of vehicle has the property of sparsity, which can be transformed to a sparse recovery problem due to compressive sensing. The accurate target localization relies on the sufficient measurements, but we cannot determine the number of measurements without knowing the number of targets. In this poster, we propose a novel target localization algorithm based on sequential compressed sensing to select the optimal number of measurements and estimate target locations via lp-norm (0<p<1) minimization. The experimental results show that our proposed algorithm has better localization performance than localization via l0 -norm or l1 -norm minimization.\",\"PeriodicalId\":344652,\"journal\":{\"name\":\"2018 IEEE International Smart Cities Conference (ISC2)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Smart Cities Conference (ISC2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISC2.2018.8656987\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Smart Cities Conference (ISC2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISC2.2018.8656987","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Target Localization Algorithm Based on Sequential Compressed Sensing for Internet of Vehicles
The grid-based target localization for internet of vehicle has the property of sparsity, which can be transformed to a sparse recovery problem due to compressive sensing. The accurate target localization relies on the sufficient measurements, but we cannot determine the number of measurements without knowing the number of targets. In this poster, we propose a novel target localization algorithm based on sequential compressed sensing to select the optimal number of measurements and estimate target locations via lp-norm (0
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
