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dc.contributor.authorSu, YTen_US
dc.contributor.authorWu, RCen_US
dc.date.accessioned2014-12-08T15:44:39Z-
dc.date.available2014-12-08T15:44:39Z-
dc.date.issued2000-11-01en_US
dc.identifier.issn0018-9545en_US
dc.identifier.urihttp://dx.doi.org/10.1109/25.901910en_US
dc.identifier.urihttp://hdl.handle.net/11536/30138-
dc.description.abstractThis paper presents mean squared error (MSE) analysis of two classes of frequency acquisition and tracking algorithms. Additive white Gaussian noise as well as Rician fading channels are considered, The class of batch-processing algorithms is an extension of earlier least squares proposals used in more benign (lower dynamic) environments. These algorithms try to fit the phase trajectory of the down-converted samples of a received signal. Such a trajectory will depend on the histories of both the signal and the local frequency variations when the local frequency is updated recursively. We propose a method to solve this difficulty and present both first-order and second-order recursive algorithms. Numerical results demonstrate that the MSE performance predicted by our analysis is consistent with that estimated by computer simulation and that the proposed algorithms not only provide rapid acquisition times but also give small tracking jitters.en_US
dc.language.isoen_USen_US
dc.subjectfrequency synchronizationen_US
dc.subjectleast squares estimationen_US
dc.subjectsatellite communicationen_US
dc.titleFrequency acquisition and tracking in high dynamic environmentsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/25.901910en_US
dc.identifier.journalIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGYen_US
dc.citation.volume49en_US
dc.citation.issue6en_US
dc.citation.spage2419en_US
dc.citation.epage2429en_US
dc.contributor.department電信工程研究所zh_TW
dc.contributor.department友訊交大聯合研發中心zh_TW
dc.contributor.departmentInstitute of Communications Engineeringen_US
dc.contributor.departmentD Link NCTU Joint Res Ctren_US
dc.identifier.wosnumberWOS:000166943100036-
dc.citation.woscount2-
Appears in Collections:Articles


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