Abstract
In its classical form, the cardinalized probability hypothesis density (CPHD) filter does not model the appearance of new targets through spawning, yet there are applications for which spawning models more appropriately account for newborn objects when compared to spontaneous birth models. In this paper, we propose a principled derivation of the CPHD filter prediction step including spontaneous birth and spawning. A Gaussian Mixture implementation of the CPHD filter with spawning is then presented, illustrated with three applicable spawning models on a simulated scenario involving two parent targets spawning a total of five objects.
Highlights
T HE goal of the multi-object estimation problem is to jointly estimate – usually in the presence of clutter, data association uncertainty, and missed detections – the time-varying number and individual states of targets evolving in a surveillance scene
While an approximation is made in [14] to circumvent evaluation of a complex integral for the implementation of a cardinalized probability hypothesis density (CPHD) filter with a Poisson spawning model, we present in this paper the CPHD time prediction equation for an arbitrary spawning process, and for three specific models
The original construction of the CPHD filter [6] does not consider a target spawning mechanism, and the key contribution of this paper is to propose its integration in the CPHD time prediction equation
Summary
T HE goal of the multi-object estimation problem is to jointly estimate – usually in the presence of clutter, data association uncertainty, and missed detections – the time-varying number and individual states of targets evolving in a surveillance scene. To improve the CPHD filter’s performance for spaceobject tracking, previous research has presented a measurementbased birth model that leverages an astrodynamics approach to track initialization for RSOs [12] While such an approach may be effective for tracking spawned RSOs, a multi-target filter that more accurately describes the physical processes that produce new RSOs through a specific spawning model is expected to provide better accuracy and faster confirmation of new objects.
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