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Authors: Silja Meyer-Nieberg ; Erik Kropat and Stefan Pickl

Affiliation: Universität der Bundeswehr München, Germany

Keyword(s): Tracking, Dynamical Systems, Particle Filter, Dynamic Optimization, Evolutionary Algorithms.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Decision Support Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Methodologies and Technologies ; Operational Research ; Optimization ; Stochastic Processes ; Symbolic Systems

Abstract: Tracking situations or more generally state estimation of dynamic systems arise in various application contexts. Usually the state-evolution equations are assumed to be known up to certain parameters. But what can be done if this is not the case? This paper presents an innovative approach to solve this difficult and complex situation by using the inherent tracking abilities of evolution strategies. Combining principles of particle filters and evolution strategies leads to a new type of algorithms: evolutionary particle filters. Their tracking quality is examined in simulations.

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Paper citation in several formats:
Meyer-Nieberg, S.; Kropat, E. and Pickl, S. (2013). Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters. In Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-8565-40-2; ISSN 2184-4372, SciTePress, pages 96-102. DOI: 10.5220/0004284300960102

@conference{icores13,
author={Silja Meyer{-}Nieberg. and Erik Kropat. and Stefan Pickl.},
title={Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters},
booktitle={Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - ICORES},
year={2013},
pages={96-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004284300960102},
isbn={978-989-8565-40-2},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - ICORES
TI - Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters
SN - 978-989-8565-40-2
IS - 2184-4372
AU - Meyer-Nieberg, S.
AU - Kropat, E.
AU - Pickl, S.
PY - 2013
SP - 96
EP - 102
DO - 10.5220/0004284300960102
PB - SciTePress

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