J. Schlichenmaier, Fabian Roos, Philipp Hügler, C. Waldschmidt
{"title":"Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis","authors":"J. Schlichenmaier, Fabian Roos, Philipp Hügler, C. Waldschmidt","doi":"10.1109/ICMIM.2019.8726765","DOIUrl":null,"url":null,"abstract":"As high resolution automotive radars become more common, so does their usage for next-generation functionalities like advanced driver assistant systems and autonomous driving. This creates the need for robust clustering techniques to distinguish among multiple extended objects like vehicles in the same scenario. One especially challenging scenario is that of separating two extended targets close to each other, each following its own trajectory. This paper proposes a clustering algorithm based on the analysis of the velocity profile to divide target points of multiple vehicles into sub-clusters. The theoretical background is explained and shown on simulation data. The algorithm is verified using radar measurements of two extended vehicular targets.","PeriodicalId":225972,"journal":{"name":"2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMIM.2019.8726765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
As high resolution automotive radars become more common, so does their usage for next-generation functionalities like advanced driver assistant systems and autonomous driving. This creates the need for robust clustering techniques to distinguish among multiple extended objects like vehicles in the same scenario. One especially challenging scenario is that of separating two extended targets close to each other, each following its own trajectory. This paper proposes a clustering algorithm based on the analysis of the velocity profile to divide target points of multiple vehicles into sub-clusters. The theoretical background is explained and shown on simulation data. The algorithm is verified using radar measurements of two extended vehicular targets.