{"title":"MUSIC算法增强分辨率的波束前预处理(预览)","authors":"H.B. Lee, M. Wengrovitz","doi":"10.1109/SPECT.1990.205564","DOIUrl":null,"url":null,"abstract":"Addresses the problem of designing a beamformer so as to enhance resolution of one or more clusters of closely-spaced emitters by the MUSIC algorithm applied in beamspace. A new beamformer design approach is presented for this problem, and is assessed for planar emitter scenarios. A metric is introduced which measures the quality of the sample beamspace MUSIC null spectrum. The approach is to construct the beamforming matrix to maximize the metric for a selected cluster. The resultant (directed) beamformer is specific to that cluster. Examples show that use of beamspace MUSIC with the identified beamformers can produce lower resolution thresholds, biases and variances than a variety of other eigenvector approaches.<<ETX>>","PeriodicalId":117661,"journal":{"name":"Fifth ASSP Workshop on Spectrum Estimation and Modeling","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Beamformer preprocessing for enhanced resolution by the MUSIC algorithm (preview)\",\"authors\":\"H.B. Lee, M. Wengrovitz\",\"doi\":\"10.1109/SPECT.1990.205564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Addresses the problem of designing a beamformer so as to enhance resolution of one or more clusters of closely-spaced emitters by the MUSIC algorithm applied in beamspace. A new beamformer design approach is presented for this problem, and is assessed for planar emitter scenarios. A metric is introduced which measures the quality of the sample beamspace MUSIC null spectrum. The approach is to construct the beamforming matrix to maximize the metric for a selected cluster. The resultant (directed) beamformer is specific to that cluster. Examples show that use of beamspace MUSIC with the identified beamformers can produce lower resolution thresholds, biases and variances than a variety of other eigenvector approaches.<<ETX>>\",\"PeriodicalId\":117661,\"journal\":{\"name\":\"Fifth ASSP Workshop on Spectrum Estimation and Modeling\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fifth ASSP Workshop on Spectrum Estimation and Modeling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPECT.1990.205564\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth ASSP Workshop on Spectrum Estimation and Modeling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPECT.1990.205564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Beamformer preprocessing for enhanced resolution by the MUSIC algorithm (preview)
Addresses the problem of designing a beamformer so as to enhance resolution of one or more clusters of closely-spaced emitters by the MUSIC algorithm applied in beamspace. A new beamformer design approach is presented for this problem, and is assessed for planar emitter scenarios. A metric is introduced which measures the quality of the sample beamspace MUSIC null spectrum. The approach is to construct the beamforming matrix to maximize the metric for a selected cluster. The resultant (directed) beamformer is specific to that cluster. Examples show that use of beamspace MUSIC with the identified beamformers can produce lower resolution thresholds, biases and variances than a variety of other eigenvector approaches.<>
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