{"title":"基于分形理论的数据关联方法","authors":"Jianhua Wang","doi":"10.1109/WCICA.2006.1713184","DOIUrl":null,"url":null,"abstract":"Fractal method is applied to resolve data association of multi-target tracking. G-P algorithm is adopted to calculate correlation dimension of measurement dimension, then measurement space is reconstructed through delay coordinate state space reconstruction method, there exists statistical similarity between reconstructed measurement and target state, relying on such similarity, data association is accomplished. Simulation results indicate that fractal algorithm has faster convergence speed and better tracking precision","PeriodicalId":375135,"journal":{"name":"2006 6th World Congress on Intelligent Control and Automation","volume":"109 22","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Data Association Method Based on Fractal Theory\",\"authors\":\"Jianhua Wang\",\"doi\":\"10.1109/WCICA.2006.1713184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fractal method is applied to resolve data association of multi-target tracking. G-P algorithm is adopted to calculate correlation dimension of measurement dimension, then measurement space is reconstructed through delay coordinate state space reconstruction method, there exists statistical similarity between reconstructed measurement and target state, relying on such similarity, data association is accomplished. Simulation results indicate that fractal algorithm has faster convergence speed and better tracking precision\",\"PeriodicalId\":375135,\"journal\":{\"name\":\"2006 6th World Congress on Intelligent Control and Automation\",\"volume\":\"109 22\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 6th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2006.1713184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 6th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2006.1713184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fractal method is applied to resolve data association of multi-target tracking. G-P algorithm is adopted to calculate correlation dimension of measurement dimension, then measurement space is reconstructed through delay coordinate state space reconstruction method, there exists statistical similarity between reconstructed measurement and target state, relying on such similarity, data association is accomplished. Simulation results indicate that fractal algorithm has faster convergence speed and better tracking precision