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dc.contributor.authorMahler, Ronald
dc.contributor.authorMaroulas, V.
dc.date.accessioned2017-08-24T02:23:36Z
dc.date.available2017-08-24T02:23:36Z
dc.date.created2017-08-23T07:21:50Z
dc.date.issued2013
dc.identifier.citationMahler, R. and Maroulas, V. 2013. Tracking spawning objects. IET Radar, Sonar and Navigation. 7 (3): pp. 321-331.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/56326
dc.identifier.doi10.1049/iet-rsn.2012.0053
dc.description.abstract

Many multi-object tracking scenarios are complicated by the fact that an object of interest may spawn additional objects which, for some period of time, follow roughly the same trajectory as the original object and then fall away. The challenge is then to discriminate the original object from the spawned ancillaries in a timely fashion. This study proposes a solution to this problem based on the increasingly well-known multi-object track-before-detect algorithm called the cardinalised probability hypothesis density (CPHD) filter. Precisely, the authors assume zero false alarms (ZFA) in the CPHD filter, and apply the proposed scheme to linear and non-linear simulation scenarios based on widely used object-trajectory and sensor models. The authors have also demonstrated that a Gaussian mixture implementation of the ZFA-CPHD filter (i) establishes stable estimates of object number, (ii) rapidly eliminates the ancillary objects and (iii) detects and accurately estimates the trajectory of the original object of interest.

dc.publisherThe Institution of Engineering and Technology
dc.titleTracking spawning objects
dc.typeJournal Article
dcterms.source.volume7
dcterms.source.number3
dcterms.source.startPage321
dcterms.source.endPage331
dcterms.source.issn1751-8784
dcterms.source.titleIET Radar, Sonar and Navigation
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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