{"title":"基于扩散的分布式MVDR波束形成器","authors":"Matthew O'Connor, W. Kleijn","doi":"10.1109/ICASSP.2014.6853709","DOIUrl":null,"url":null,"abstract":"Advances in hardware and communication technology make distributed sound acquisition increasingly attractive. We describe a distributed beamforming method based on the diffusion adaptation paradigm. In contrast to existing distributed beamforming methods, the method does not impose conditions on the topology or the structure of the network nor does it require knowledge of the noise co-variance matrix. The algorithm can continuously track changes in the noise covariance matrix, making it suitable for a practical, dynamic environment. It will typically perform one iteration per signal sample, limiting communication requirements. Our experiments confirm the effectiveness of the method.","PeriodicalId":6545,"journal":{"name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"29 1","pages":"810-814"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Diffusion-based distributed MVDR beamformer\",\"authors\":\"Matthew O'Connor, W. Kleijn\",\"doi\":\"10.1109/ICASSP.2014.6853709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Advances in hardware and communication technology make distributed sound acquisition increasingly attractive. We describe a distributed beamforming method based on the diffusion adaptation paradigm. In contrast to existing distributed beamforming methods, the method does not impose conditions on the topology or the structure of the network nor does it require knowledge of the noise co-variance matrix. The algorithm can continuously track changes in the noise covariance matrix, making it suitable for a practical, dynamic environment. It will typically perform one iteration per signal sample, limiting communication requirements. Our experiments confirm the effectiveness of the method.\",\"PeriodicalId\":6545,\"journal\":{\"name\":\"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"volume\":\"29 1\",\"pages\":\"810-814\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2014.6853709\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2014.6853709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Advances in hardware and communication technology make distributed sound acquisition increasingly attractive. We describe a distributed beamforming method based on the diffusion adaptation paradigm. In contrast to existing distributed beamforming methods, the method does not impose conditions on the topology or the structure of the network nor does it require knowledge of the noise co-variance matrix. The algorithm can continuously track changes in the noise covariance matrix, making it suitable for a practical, dynamic environment. It will typically perform one iteration per signal sample, limiting communication requirements. Our experiments confirm the effectiveness of the method.