{"title":"稀疏最小均方误差(MMSE)盲波束形成器","authors":"Hua Peng, N. Al-Dhahir","doi":"10.1109/IEEE-IWS.2015.7164517","DOIUrl":null,"url":null,"abstract":"To reduce implementation cost and power consumption, a sparse minimum mean square error (MMSE) beamformer is designed using the orthogonal matching pursuit algorithm to compute the locations and weights of few selected active antenna elements and estimate the direction-of-arrival (DOA) angles and the number of sources. The estimated DOAs are used to reconstruct the received signal covariance matrix and improve robustness to small sample size effects. Simulation results demonstrate the superior performance of the proposed sparse beamformer designs compared with state-of-the-art algorithms in the literature.","PeriodicalId":164534,"journal":{"name":"2015 IEEE International Wireless Symposium (IWS 2015)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sparse minimum mean square error(MMSE) blind beamformer\",\"authors\":\"Hua Peng, N. Al-Dhahir\",\"doi\":\"10.1109/IEEE-IWS.2015.7164517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To reduce implementation cost and power consumption, a sparse minimum mean square error (MMSE) beamformer is designed using the orthogonal matching pursuit algorithm to compute the locations and weights of few selected active antenna elements and estimate the direction-of-arrival (DOA) angles and the number of sources. The estimated DOAs are used to reconstruct the received signal covariance matrix and improve robustness to small sample size effects. Simulation results demonstrate the superior performance of the proposed sparse beamformer designs compared with state-of-the-art algorithms in the literature.\",\"PeriodicalId\":164534,\"journal\":{\"name\":\"2015 IEEE International Wireless Symposium (IWS 2015)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Wireless Symposium (IWS 2015)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEE-IWS.2015.7164517\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Wireless Symposium (IWS 2015)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEE-IWS.2015.7164517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sparse minimum mean square error(MMSE) blind beamformer
To reduce implementation cost and power consumption, a sparse minimum mean square error (MMSE) beamformer is designed using the orthogonal matching pursuit algorithm to compute the locations and weights of few selected active antenna elements and estimate the direction-of-arrival (DOA) angles and the number of sources. The estimated DOAs are used to reconstruct the received signal covariance matrix and improve robustness to small sample size effects. Simulation results demonstrate the superior performance of the proposed sparse beamformer designs compared with state-of-the-art algorithms in the literature.