Maintaining track identity for a fragmenting target

Gardner, A.; Rollason, Malcolm P.; Everett, Nick; Salmond, David

doi:10.1049/ic:20060554

Cited by 1 publication

(2 citation statements)

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“…Both scenarios are much more difficult, and general than Isaac et al ’s approach [5, 6], where the number of objects can increase by at most one. Furthermore, Gardner et al [4] assume which target generates which measurement. This is an impractical assumption.…”

Section: Algorithm Using the Gaussian‐mixture Zfa‐cphd Filtermentioning

confidence: 99%

“…We have been able to find only a small number of open‐literature papers that address this or related problems. Gardner et al [4] employ Kalman filters to track spawned objects, but assume that the measurement‐to‐track association problem has been resolved. That is, given a sequence of measurement, it is known a priori which object generated which measurement.…”

Section: Introductionmentioning

confidence: 99%

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Tracking spawning objects

Mahler

Maroulas

2013

IET Radar, Sonar & Navigation

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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.

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Section: Algorithm Using the Gaussian‐mixture Zfa‐cphd Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%