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Simulations Using the Kalman Filter

Vascimini, Vincent G

Abstract Details

2020, Master of Arts (MA), Bowling Green State University, Mathematics.
Control and estimation theory are branches of mathematics that involve using data and measurements to estimate the value of a parameter of interest, and how changing certain parameters effects this estimation. The Kalman filter is a fundamental result in control and estimation theory that was introduced by Rudolf E. Kalman in 1960. The Kalman filter is a set of equations that provides an optimal estimate of the state of a system in a least-squares sense. The filter is often sought for its recursive and noise-smoothing properties, and has been found useful across many disciplines and in real world systems. This thesis will contribute to the literature of control and estimation theory by providing an introduction to the principles of the filter. This introduction includes a brief history of the filter, a derivation of the filter equations, and simple examples of applications of the filter.
Kit Chan, Dr. (Advisor)
So-Hsiang Chou, Dr. (Committee Member)
54 p.

Recommended Citations

Citations

  • Vascimini, V. G. (2020). Simulations Using the Kalman Filter [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1576685726771127

    APA Style (7th edition)

  • Vascimini, Vincent. Simulations Using the Kalman Filter. 2020. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1576685726771127.

    MLA Style (8th edition)

  • Vascimini, Vincent. "Simulations Using the Kalman Filter." Master's thesis, Bowling Green State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1576685726771127

    Chicago Manual of Style (17th edition)