{"title":"DOA estimation under sensor gain and phase uncertainties","authors":"Luming Zhao, Hongqing Liu, Yong Li, Yi Zhou","doi":"10.1109/ICEDIF.2015.7280192","DOIUrl":null,"url":null,"abstract":"This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of sensor's gain and phase uncertainties. The main motivation in our work is to explore the signal sparse property. The DOA sparsity is relatively easy to be discovered since the spectrum of signal is sparse in spatial domain. One existing finding on uncertainty matrix is it is a sparse matrix since it only is diagonal. To combat the gain and phase uncertainties, a direct estimate of uncertainty matrix is developed by utilizing its sparse property. By exploring the sparsity of both the DOAs and uncertainties of gain and phase, a two-step iterative process is proposed to achieve a joint estimation of DOAs and gain and phase uncertainties. Numerical studies are presented to demonstrate the effectiveness of the joint estimation.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"211 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEDIF.2015.7280192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of sensor's gain and phase uncertainties. The main motivation in our work is to explore the signal sparse property. The DOA sparsity is relatively easy to be discovered since the spectrum of signal is sparse in spatial domain. One existing finding on uncertainty matrix is it is a sparse matrix since it only is diagonal. To combat the gain and phase uncertainties, a direct estimate of uncertainty matrix is developed by utilizing its sparse property. By exploring the sparsity of both the DOAs and uncertainties of gain and phase, a two-step iterative process is proposed to achieve a joint estimation of DOAs and gain and phase uncertainties. Numerical studies are presented to demonstrate the effectiveness of the joint estimation.