{"title":"Subspace method to estimate parameters of wideband polynomial-phase signals in sensor arrays","authors":"Chenlei Li, Mei Liu, Pengfei Wang, He Wang","doi":"10.1109/ICEDIF.2015.7280187","DOIUrl":null,"url":null,"abstract":"A novel subspace method for estimating the parameters of wideband polynomial-phase signals (PPSs) in sensor arrays that exploits the characteristics of the high-order instantaneous moment (HIM) to form a model of signals received by an array is presented. The super-resolution and robustness of subspace theory is employed to estimate the direction of arrival (DOA) and coefficients of the Kth-order PPS. This method, which has lower computational complexity than maximum likelihood (ML), can reduce error propagation and provide more precise estimation than conventional high-order ambiguity function (HAF) methods, as demonstrated by simulation results.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","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.2015.7280187","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
A novel subspace method for estimating the parameters of wideband polynomial-phase signals (PPSs) in sensor arrays that exploits the characteristics of the high-order instantaneous moment (HIM) to form a model of signals received by an array is presented. The super-resolution and robustness of subspace theory is employed to estimate the direction of arrival (DOA) and coefficients of the Kth-order PPS. This method, which has lower computational complexity than maximum likelihood (ML), can reduce error propagation and provide more precise estimation than conventional high-order ambiguity function (HAF) methods, as demonstrated by simulation results.
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