A CPHD Filter for Tracking With Spawning Models
Journal article, 2013

In some applications of multi-target tracking, appearing targets are suitably modeled as spawning from existing targets. However, in the original formulation of the cardinalized probability hypothesis density (CPHD) filter, this type of model is not supported; instead appearing targets are modeled by spontaneous birth only. In this paper we derive the necessary equations for a CPHD filter for the case when the process model also includes target spawning. For this generalized filter, the cardinality prediction formula might become computationally intractable for general spawning models. However, when the cardinality distribution of the spawning targets is either Bernoulli or Poisson, we derive expressions that are practical and computationally efficient. Simulations show that the proposed filter responds faster to a change in target number due to spawned targets than the original CPHD filter. In addition, the performance of the filter, considering the optimal subpattern assignment (OSPA), is improved when having an explicit spawning model.

filtering theory

spatial

recursive estimation

multiple targets

hypothesis density filter

point-processes

Bayesian methods

probabilistic data association

Author

Malin Lundgren

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lars Hammarstrand

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Journal on Selected Topics in Signal Processing

1932-4553 (ISSN)

Vol. 7 3 496-507 6479228

Subject Categories

Signal Processing

DOI

10.1109/JSTSP.2013.2252599

More information

Created

10/7/2017