Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISIT.2007.4557479
DC FieldValue
dc.titleEstimating the frequency and phase of a noisy sinusoid by Kalman filter
dc.contributor.authorKam, P.Y.
dc.contributor.authorFu, H.
dc.date.accessioned2014-04-24T08:34:57Z
dc.date.available2014-04-24T08:34:57Z
dc.date.issued2007
dc.identifier.citationKam, P.Y., Fu, H. (2007). Estimating the frequency and phase of a noisy sinusoid by Kalman filter. IEEE International Symposium on Information Theory - Proceedings : 1781-1785. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIT.2007.4557479
dc.identifier.isbn1424414296
dc.identifier.issn21578101
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51158
dc.description.abstractA linear, two-dimensional state-space model involving the instantaneous signal frequency and carrier phase is formulated. This enables Kalman filtering to be used for estimating the frequency and phase. Two Kalman filters are presented here, one based on the old observation model of Tretter [3], and the other based on our newly proposed model in [1]. The Kalman filter for the old observation model requires knowledge of the signal amplitude and the noise variance, while for the new observation model, only knowledge of the noise variance is required. Their mean square estimation error performances are compared using simulations, and it is shown that the filter based on the new observation model performs better, especially at low signal-to-noise ratio. Kalman filtering also allows the incorporation of prior knowledge of the interval of distribution of the frequency to improve the estimation performance. ©2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ISIT.2007.4557479
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ISIT.2007.4557479
dc.description.sourcetitleIEEE International Symposium on Information Theory - Proceedings
dc.description.page1781-1785
dc.description.codenPISTF
dc.identifier.isiut000257010202018
Appears in Collections:Staff Publications

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