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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorShen, C. N., 1917-
dc.contributorModestino, James W.
dc.contributorDeRusso, Paul M. (Paul Madden)
dc.contributorAmazigo, John C.
dc.contributor.authorSonalkar, Ranjan V.
dc.date.accessioned2021-11-03T08:44:50Z
dc.date.available2021-11-03T08:44:50Z
dc.date.created2017-02-10T11:07:06Z
dc.date.issued1975-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1876
dc.descriptionDecember 1975
dc.descriptionSchool of Engineering
dc.description.abstractSufficient conditions for convergence of standard Kalman filter estimating the states of a system subject to unknown inputs have been postulated and proved; and the limit of signal to noise ratio under which the proposed scheme will not be advantageous, have been derived. The scheme has been applied to the development of an obstacle detection algorithm for the proposed Mars rover, and a simultaneous Bayesian estimate of states and inputs is derived to point direction for future improvement of the presently suboptimal input estimation.
dc.description.abstractThis Thesis describes a decision directed adaptive discrete filter for rapid estimation of states for systems subject to unknown input sequences, from noisy observations. Divergence of Kalman filter is a foreseen possibility when the states of a partially or incorrectly known system are being estimated. Therefore, possible divergence is prevented by modeling the uncertainties in the system as a finite sequence of unknown inputs and incorporating Bayes decision rule to detect the inputs. The scheme is applicable to arbitrary inputs of known form. In case of inputs with known evolution, a state augmented approach is presented to make the estimation near-optimal. However, due to the bank of four, three-stage filtered estimates being calculated simultaneously, the scheme is applicable subject to the availability of sufficient computation capabilities
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectElectrical and systems engineering
dc.titleA decision-directed rapid estimation of states for systems subject to unknown input sequences
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid177963
dc.digitool.pid177964
dc.digitool.pid177965
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Electrical and Systems Engineering


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