{"title":"天线间距较大的非相干MIMO雷达多目标定位","authors":"Yue Ai, Wei Yi, G. Cui, L. Kong","doi":"10.1109/RADAR.2014.6875793","DOIUrl":null,"url":null,"abstract":"This paper addresses the multi-target localization problem for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. To this end, we first adopt a high-dimensional parameter vector, which is the concatenation of the parameters to be estimated for individual targets, and then propose a novel multi-target localization algorithm by estimating the high-dimensional parameter vector based on maximum-likelihood estimation (MLE). However, this solution is usually computationally intractable for most realistic problems as it is involved with a high-dimensional joint maximization. Therefore we also propose a suboptimum algorithm which allows trading better localization accuracy for a much lower implementation complexity. Numerical examples are provided to assess and compare the performances of the proposed multi-target localization algorithms.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Multi-target localization for noncoherent MIMO radar with widely separated antennas\",\"authors\":\"Yue Ai, Wei Yi, G. Cui, L. Kong\",\"doi\":\"10.1109/RADAR.2014.6875793\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses the multi-target localization problem for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. To this end, we first adopt a high-dimensional parameter vector, which is the concatenation of the parameters to be estimated for individual targets, and then propose a novel multi-target localization algorithm by estimating the high-dimensional parameter vector based on maximum-likelihood estimation (MLE). However, this solution is usually computationally intractable for most realistic problems as it is involved with a high-dimensional joint maximization. Therefore we also propose a suboptimum algorithm which allows trading better localization accuracy for a much lower implementation complexity. Numerical examples are provided to assess and compare the performances of the proposed multi-target localization algorithms.\",\"PeriodicalId\":127690,\"journal\":{\"name\":\"2014 IEEE Radar Conference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Radar Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RADAR.2014.6875793\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Radar Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2014.6875793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-target localization for noncoherent MIMO radar with widely separated antennas
This paper addresses the multi-target localization problem for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. To this end, we first adopt a high-dimensional parameter vector, which is the concatenation of the parameters to be estimated for individual targets, and then propose a novel multi-target localization algorithm by estimating the high-dimensional parameter vector based on maximum-likelihood estimation (MLE). However, this solution is usually computationally intractable for most realistic problems as it is involved with a high-dimensional joint maximization. Therefore we also propose a suboptimum algorithm which allows trading better localization accuracy for a much lower implementation complexity. Numerical examples are provided to assess and compare the performances of the proposed multi-target localization algorithms.