{"title":"一种鲁棒高分辨率DOA估计新方法","authors":"P. Totarong, A. El-Jaroudi","doi":"10.1109/SSAP.1992.246774","DOIUrl":null,"url":null,"abstract":"The proposed method is based on the signal eigenvectors of the covariance matrix being a linear combination of the direction vectors which contain the DOA information. By applying a high-resolution frequency estimation algorithm to an element sequence of a combination of signal eigenvectors, the proposed method has better performance at low SNR. For example, it is about 5 dB more robust to noise than spatial-smoothed MUSIC and Minimum-Norm.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A novel approach for robust high-resolution DOA estimation\",\"authors\":\"P. Totarong, A. El-Jaroudi\",\"doi\":\"10.1109/SSAP.1992.246774\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proposed method is based on the signal eigenvectors of the covariance matrix being a linear combination of the direction vectors which contain the DOA information. By applying a high-resolution frequency estimation algorithm to an element sequence of a combination of signal eigenvectors, the proposed method has better performance at low SNR. For example, it is about 5 dB more robust to noise than spatial-smoothed MUSIC and Minimum-Norm.<<ETX>>\",\"PeriodicalId\":309407,\"journal\":{\"name\":\"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSAP.1992.246774\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSAP.1992.246774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel approach for robust high-resolution DOA estimation
The proposed method is based on the signal eigenvectors of the covariance matrix being a linear combination of the direction vectors which contain the DOA information. By applying a high-resolution frequency estimation algorithm to an element sequence of a combination of signal eigenvectors, the proposed method has better performance at low SNR. For example, it is about 5 dB more robust to noise than spatial-smoothed MUSIC and Minimum-Norm.<>