{"title":"三维和四维矢量传感器分布式网络的自适应扩散四元数LMS算法","authors":"C. Jahanchahi, D. Mandic","doi":"10.5281/ZENODO.43726","DOIUrl":null,"url":null,"abstract":"A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion division algebra and the widely linear model allow for a unified processing of 3D and 4D data, which can exhibit both circular and noncircular distributions. The analysis shows that the D-WLIQLMS provides a solution that is robust to link and node failures in sensor networks. Simulations on benchmark 4D signals illustrate the advantages offered by the D-WLIQLMS.","PeriodicalId":400766,"journal":{"name":"21st European Signal Processing Conference (EUSIPCO 2013)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"An adaptive diffusion quaternion LMS algorithm for distributed networks of 3D and 4D vector sensors\",\"authors\":\"C. Jahanchahi, D. Mandic\",\"doi\":\"10.5281/ZENODO.43726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion division algebra and the widely linear model allow for a unified processing of 3D and 4D data, which can exhibit both circular and noncircular distributions. The analysis shows that the D-WLIQLMS provides a solution that is robust to link and node failures in sensor networks. Simulations on benchmark 4D signals illustrate the advantages offered by the D-WLIQLMS.\",\"PeriodicalId\":400766,\"journal\":{\"name\":\"21st European Signal Processing Conference (EUSIPCO 2013)\",\"volume\":\"319 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"21st European Signal Processing Conference (EUSIPCO 2013)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.43726\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"21st European Signal Processing Conference (EUSIPCO 2013)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.43726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive diffusion quaternion LMS algorithm for distributed networks of 3D and 4D vector sensors
A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion division algebra and the widely linear model allow for a unified processing of 3D and 4D data, which can exhibit both circular and noncircular distributions. The analysis shows that the D-WLIQLMS provides a solution that is robust to link and node failures in sensor networks. Simulations on benchmark 4D signals illustrate the advantages offered by the D-WLIQLMS.