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dc.contributor.authorSolberg, Lars Erik
dc.contributor.authorHamran, Svein-Erik
dc.contributor.authorBerger, Tor
dc.contributor.authorBalasingham, Ilangko
dc.date.accessioned2017-10-30T13:29:55Z
dc.date.accessioned2017-10-31T09:33:50Z
dc.date.available2017-10-30T13:29:55Z
dc.date.available2017-10-31T09:33:50Z
dc.date.issued2010
dc.identifier.citationSolberg LE, Hamran S-E, Berger T, Balasingham IB. Minimum Variance Signal Selection for Aorta Radius Estimation Using Radar. EURASIP Journal on Advances in Signal Processing. 2010en_GB
dc.identifier.urihttp://hdl.handle.net/20.500.12242/761
dc.identifier.urihttps://ffi-publikasjoner.archive.knowledgearc.net/handle/20.500.12242/761
dc.descriptionSolberg, Lars Erik; Hamran, Svein-Erik; Berger, Tor; Balasingham, Ilangko. Minimum Variance Signal Selection for Aorta Radius Estimation Using Radar. EURASIP Journal on Advances in Signal Processing 2010 s. -en_GB
dc.description.abstractThis paper studies the optimum signal choice for the estimation of the aortic blood pressure via aorta radius, using a monostatic radar configuration. The method involves developing the Cramér-Rao lower bound (CRLB) for a simplified model. The CRLB for model parameters are compared with simulation results using a grid-based approach for estimation. The CRLBs are within the 99% confidence intervals for all chosen parameter values. The CRLBs show an optimal region within an ellipsoid centered at 1 GHz center frequency and 1.25 GHz bandwidth with axes of 0.5 GHz and 1 GHz, respectively. Calculations show that emitted signal energy to received noise spectral density should exceed for a precision of approximately 0.1 mm for a large range of model parameters. This implies a minimum average power of 0.4  . These values are based on optimistic assumptions. Reflections, improved propagation model, true receiver noise, and parameter ranges should be considered in a practical implementation.en_GB
dc.language.isoenen_GB
dc.subjectRadar
dc.subjectKreft
dc.titleMinimum Variance Signal Selection for Aorta Radius Estimation Using Radaren_GB
dc.typeArticleen_GB
dc.date.updated2017-10-30T13:29:54Z
dc.identifier.cristinID515065
dc.identifier.cristinID515065
dc.identifier.doi10.1155/2010/682037
dc.source.issn1687-6172
dc.source.issn1687-6180
dc.type.documentJournal article
dc.relation.journalEURASIP Journal on Advances in Signal Processing


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