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Particle Filtering Approach for Data Association
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

An initial work has been performed to implement a sequential Monte Carlo method to solve the data association problem. The main motivation is to overcome the incorrect association when the state estimates are inaccurate. The solution is based on modeling the data association as a stochastic variable and estimated with a bootstrap particle filter. Two variants of the proposal function are evaluated, one with the uniform distribution over possible associations, and the other one with the distribution depending on the measurements and state estimates. The performance of both proposals is evaluated on the small simulation example, and compared to a purely deterministic approach, Nearest-Neighbour, as well. The obtained initial results are quite promising, and more evaluation and expansion to more examples and real data sets is suggested for the future work.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Data Association, Particle Filter, Sequential Monte Carlo
National Category
Engineering and Technology Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-159327OAI: oai:DiVA.org:liu-159327DiVA, id: diva2:1341074
Conference
22nd International Conference on Information Fusion, Ottawa, Canada, July 2-5, 2019
Projects
LINK-SICAvailable from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-16Bibliographically approved

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Sjanic, Zoran

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