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    A decision-directed rapid estimation of states for systems subject to unknown input sequences

    Author
    Sonalkar, Ranjan V.
    View/Open
    177964_thesis.pdf (5.271Mb)
    Other Contributors
    Shen, C. N., 1917-; Modestino, James W.; DeRusso, Paul M. (Paul Madden); Amazigo, John C.;
    Date Issued
    1975-12
    Subject
    Electrical Systems engineering
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/1876
    Abstract
    Sufficient 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.; This 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;
    Description
    December 1975; School of Engineering
    Department
    Dept. of Electrical and Systems Engineering;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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