Constrained multiple model bayesian filtering for target tracking in cluttered environment

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dc.contributor.authorHe, Shaoming
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2017-11-07T14:33:07Z
dc.date.available2017-11-07T14:33:07Z
dc.date.issued2017-10-18
dc.description.abstractThis paper proposes a composite Bayesian filtering approach for unmanned aerial vehicle trajectory estimation in cluttered environments. More specifically, a complete model for the measurement likelihood function of all measurements, including target-generated observation and false alarms, is derived based on the random finite set theory. To accommodate several different manoeuvre modes and system state constraints, a recursive multiple model Bayesian filtering algorithm and its corresponding Sequential Monte Carlo implementation are established. Compared with classical approaches, the proposed method addresses the problem of measurement uncertainty without any data associations. Numerical simulations for estimating an unmanned aerial vehicle trajectory generated by generalised proportional navigation guidance law clearly demonstrate the effectiveness of the proposed formulation.en_UK
dc.identifier.citationShaoming H, Shin H-S, Tsourdos A, Constrained multiple model bayesian filtering for target tracking in cluttered environment, IFAC papers online, Vol. 50, Issue 1, July 2017, pp. 425-430en_UK
dc.identifier.issn1474-6670
dc.identifier.urihttp://dx.doi.org/10.1016/j.ifacol.2017.08.192
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/12702
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectUnmanned aerial vehicleen_UK
dc.subjectTrajectory estimationen_UK
dc.subjectRandom finite seten_UK
dc.subjectMultiple model filteringen_UK
dc.subjectSystem state constrainten_UK
dc.subjectSequential Monte Carlo implementationen_UK
dc.titleConstrained multiple model bayesian filtering for target tracking in cluttered environmenten_UK
dc.typeArticleen_UK

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