{"title":"到达方向的多重协方差矩阵谱模型","authors":"H. Alnajjar, D. Wilkes","doi":"10.1109/SECON.1995.513126","DOIUrl":null,"url":null,"abstract":"Presents a novel algorithm for modeling the behavior of the measured eigenvalues in order to find the direction of arrivals (DOAs) of sources. The authors use only the eigenvalues and not the eigenvectors to find the DOAs; no other algorithm works this way. The modeling process is based on the critical distance formula developed previously by the authors, which describes the best location to add a sensor to an existing subarray in order to improve the resolution performance of an array, also it is based on the concept of a structurally adaptive array, which promotes the idea of adapting the size and geometry of a subarray (of a much larger array) in order to optimize the detection for different scenarios.","PeriodicalId":334874,"journal":{"name":"Proceedings IEEE Southeastcon '95. Visualize the Future","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiple covariance matrix spectral model for direction of arrival\",\"authors\":\"H. Alnajjar, D. Wilkes\",\"doi\":\"10.1109/SECON.1995.513126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Presents a novel algorithm for modeling the behavior of the measured eigenvalues in order to find the direction of arrivals (DOAs) of sources. The authors use only the eigenvalues and not the eigenvectors to find the DOAs; no other algorithm works this way. The modeling process is based on the critical distance formula developed previously by the authors, which describes the best location to add a sensor to an existing subarray in order to improve the resolution performance of an array, also it is based on the concept of a structurally adaptive array, which promotes the idea of adapting the size and geometry of a subarray (of a much larger array) in order to optimize the detection for different scenarios.\",\"PeriodicalId\":334874,\"journal\":{\"name\":\"Proceedings IEEE Southeastcon '95. Visualize the Future\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE Southeastcon '95. Visualize the Future\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SECON.1995.513126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE Southeastcon '95. Visualize the Future","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.1995.513126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiple covariance matrix spectral model for direction of arrival
Presents a novel algorithm for modeling the behavior of the measured eigenvalues in order to find the direction of arrivals (DOAs) of sources. The authors use only the eigenvalues and not the eigenvectors to find the DOAs; no other algorithm works this way. The modeling process is based on the critical distance formula developed previously by the authors, which describes the best location to add a sensor to an existing subarray in order to improve the resolution performance of an array, also it is based on the concept of a structurally adaptive array, which promotes the idea of adapting the size and geometry of a subarray (of a much larger array) in order to optimize the detection for different scenarios.