2026-09-19T07:26:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1014962022-01-13T07:53:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hamsa Balakrishnan.
Kasperski, Michael William
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2016-03-03T21:04:51Z
2016-03-03T21:04:51Z
2015
2015
http://hdl.handle.net/1721.1/101496
939658980
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-64).
This thesis investigates the field of stochastic hybrid estimation. A broad introduction to the framework surrounding estimation, filtering, and multiple model based systems is presented. More specifically, the often made assumption of a constant time-invariant mode transition probability matrix is relaxed. Recent work done in the area of non-Markov jump stochastic hybrid systems is explored, including semi- Markov systems, non-homogeneous transition probability matrices, and continuous-state-dependent mode transitions. Algorithms needed to develop linear multiple model based filters with non-homogeneous transition probabilities are detailed. Finally, a case study for the practical implementation of an extended Kalman filter in the application of attitude heading and reference systems is conducted.
by Michael William Kasperski.
S.M.
64 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Multiple model estimation for linear stochastic hybrid systems with non-homogeneous transition probabilities
Thesis
application/pdf
Publication
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