{"title":"基于EKF的MIMO传播参数跟踪","authors":"J. Salmi, A. Richter, V. Koivunen","doi":"10.1109/NSSPW.2006.4378822","DOIUrl":null,"url":null,"abstract":"In this paper we describe the application of extracting the MIMO radio channel propagation parameters from channel sounding measurements using the Extended Kalman Filter. This approach allows to capture the dynamics of the radio propagation channels and enables recursive, computationally low-complexity (compared with traditional iterative maximum likelihood based methods) estimation of the parameters. We also discuss the selection of the state dimension, i.e., the appropriate number of propagation paths to track.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"MIMO Propagation Parameter Tracking using EKF\",\"authors\":\"J. Salmi, A. Richter, V. Koivunen\",\"doi\":\"10.1109/NSSPW.2006.4378822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we describe the application of extracting the MIMO radio channel propagation parameters from channel sounding measurements using the Extended Kalman Filter. This approach allows to capture the dynamics of the radio propagation channels and enables recursive, computationally low-complexity (compared with traditional iterative maximum likelihood based methods) estimation of the parameters. We also discuss the selection of the state dimension, i.e., the appropriate number of propagation paths to track.\",\"PeriodicalId\":388611,\"journal\":{\"name\":\"2006 IEEE Nonlinear Statistical Signal Processing Workshop\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Nonlinear Statistical Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSSPW.2006.4378822\",\"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 IEEE Nonlinear Statistical Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSPW.2006.4378822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we describe the application of extracting the MIMO radio channel propagation parameters from channel sounding measurements using the Extended Kalman Filter. This approach allows to capture the dynamics of the radio propagation channels and enables recursive, computationally low-complexity (compared with traditional iterative maximum likelihood based methods) estimation of the parameters. We also discuss the selection of the state dimension, i.e., the appropriate number of propagation paths to track.