{"title":"Target Localization in Distributed MIMO Radar from Time Delays, Doppler Shifts, Azimuth and Elevation Angles of Arrival","authors":"A. Noroozi, Mohammad Mahdi Navebi, R. Amiri","doi":"10.1109/IranianCEE.2019.8786435","DOIUrl":null,"url":null,"abstract":"In this paper, we focus on the moving target localization problem in a multiple-input multiple-output radar with widely separated antennas. By exploiting jointly different types of information including time delay, Doppler shift and azimuth and elevation angles of arrival, we develop an algebraic closed-form two-stage weighted least squares solution for the problem. The proposed algorithm is shown analytically to attain the CramerRao lower bound accuracy under the small Gaussian noise assumption. Numerical simulations are included to examine the algorithm's performance and corroborate the theoretical developments.","PeriodicalId":6683,"journal":{"name":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","volume":"30 1","pages":"1498-1503"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IranianCEE.2019.8786435","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we focus on the moving target localization problem in a multiple-input multiple-output radar with widely separated antennas. By exploiting jointly different types of information including time delay, Doppler shift and azimuth and elevation angles of arrival, we develop an algebraic closed-form two-stage weighted least squares solution for the problem. The proposed algorithm is shown analytically to attain the CramerRao lower bound accuracy under the small Gaussian noise assumption. Numerical simulations are included to examine the algorithm's performance and corroborate the theoretical developments.