{"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}
引用次数: 8
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.