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dc.contributor.authorGodbaz, John Peter
dc.contributor.authorCree, Michael J.
dc.contributor.authorDorrington, Adrian A.
dc.contributor.authorPayne, Andrew D.
dc.coverage.spatialConference held at Christchurch, New Zealanden_NZ
dc.date.accessioned2010-05-11T21:37:30Z
dc.date.available2010-05-11T21:37:30Z
dc.date.issued2009
dc.identifier.citationGodbaz, J.P., Cree, M.J., Dorrington, A.A. & Payne, A.D. (2009). A fast Maximum Likelihood method for improving AMCW lidar precision using waveform shape. In proceedings of IEEE 2009 Sensors Conference, 25-28 Oct 2009, Christchurch, New Zealand. (pp. 735-738). Washington, USA: IEEE.en
dc.identifier.urihttps://hdl.handle.net/10289/3873
dc.description.abstractAmplitude Modulated Continuous Wave imaging lidar systems use the time-of-flight principle to determine the range to objects in a scene. Typical systems use modulated illumination of a scene and a modulated sensor or image intensifier. By changing the relative phase of the two modulation signals it is possible to measure the phase shift induced in the illumination signal, thus the range to the scene. In practical systems, the resultant correlation waveform contains harmonics that typically result in a non-linear range response. Nevertheless, these harmonics can be used to improve range precision. We model a waveform continuously variable in phase and intensity as a linear interpolation. By approximating the problem as a Maximum Likelihood problem, an analytic solution for the problem is derived that enables an entire range image to be processed in a few seconds. A substantial improvement in overall RMS error and precision over the standard Fourier phase analysis approach results.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rights© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
dc.sourceIEEE Sensors Conferenceen_NZ
dc.subjectMaximum Likelihood methoden
dc.subjectRMS erroren
dc.subjectlidar systemen
dc.titleA fast Maximum Likelihood method for improving AMCW lidar precision using waveform shapeen
dc.typeConference Contributionen
dc.identifier.doi10.1109/ICSENS.2009.5398544en
dc.relation.isPartOf2009 IEEE SENSORS, VOLS 1-3en_NZ
pubs.begin-page735en_NZ
pubs.elements-id19255
pubs.end-page738en_NZ
pubs.finish-date2009-10-28en_NZ
pubs.start-date2009-10-25en_NZ


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