{"title":"稀疏数组MUSIC变量的比较","authors":"K. Adhikari, Benjamin Drozdenko","doi":"10.1109/NAECON46414.2019.9058169","DOIUrl":null,"url":null,"abstract":"Nested and coprime arrays have high degrees of freedom that can be exploited in direction of arrival estimation using various algorithms. Most algorithms use a combination of product processing, min processing, and MUSIC. We show that direct MUSIC with unbiased autocorrelations estimates is superior to other algorithms.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Comparison of MUSIC Variants for Sparse Arrays\",\"authors\":\"K. Adhikari, Benjamin Drozdenko\",\"doi\":\"10.1109/NAECON46414.2019.9058169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nested and coprime arrays have high degrees of freedom that can be exploited in direction of arrival estimation using various algorithms. Most algorithms use a combination of product processing, min processing, and MUSIC. We show that direct MUSIC with unbiased autocorrelations estimates is superior to other algorithms.\",\"PeriodicalId\":193529,\"journal\":{\"name\":\"2019 IEEE National Aerospace and Electronics Conference (NAECON)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE National Aerospace and Electronics Conference (NAECON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAECON46414.2019.9058169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON46414.2019.9058169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nested and coprime arrays have high degrees of freedom that can be exploited in direction of arrival estimation using various algorithms. Most algorithms use a combination of product processing, min processing, and MUSIC. We show that direct MUSIC with unbiased autocorrelations estimates is superior to other algorithms.