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Parallelization of the Kalman filter on multicore computational platforms
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2013 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 9, 1188-1194 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2013. Vol. 21, no 9, 1188-1194 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-207009DOI: 10.1016/j.conengprac.2013.03.008ISI: 000322295600004OAI: oai:DiVA.org:uu-207009DiVA: diva2:647150
Available from: 2013-06-13 Created: 2013-09-09 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Parallelization of stochastic estimation algorithms on multicore computational platforms
Open this publication in new window or tab >>Parallelization of stochastic estimation algorithms on multicore computational platforms
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The main part of this licentiate thesis concerns parallelization of recursive estimation methods, both linear and nonlinear. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in many applications of signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is known to be computationally expensive, which often makes the methods infeasible in real-time applications and for problems of large dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is the most powerful way of improving the execution time of an algorithm. It has been found in this thesis that several of the optimal filtering methods are suitable for parallel implementation, in certain ranges of problem sizes. It has been concluded from the experiments that substantial improvements can be achieved by performing "tailor"-made parallelization, compared to straightforward implementations based on multi-threaded libraries. For many of the suggested parallelizations, a linear speedup in the number of cores has been achieved that have provided up to 8 times speedup on a double quad-core computer. As the evolution of the parallel computer architectures is unfolding rapidly, many more processors on the same chip will become available. The developed methods do not, of course, scale infinitely, but definitely can exploit and harness some of the computational power of the next generation of parallel platforms, allowing for optimal state estimation in real-time applications.

Place, publisher, year, edition, pages
Uppsala University, 2013
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2013-001
National Category
Control Engineering Computer Science
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-227637 (URN)
Supervisors
Available from: 2013-04-19 Created: 2014-06-29 Last updated: 2017-08-31Bibliographically approved

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Rosén, OlovMedvedev, AlexanderWigren, Torbjörn

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