Multiple Extended Target Tracking With Labeled Random Finite Sets
Journal article, 2016

Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the standard measurement model. In this paper, a new algorithm is proposed for tracking multiple extended targets in clutter, which is capable of estimating the number of targets, as well the trajectories of their states, comprising the kinematics, measurement rates, and extents. The proposed technique is based on modeling the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set (RFS), within which the extended targets are modeled using gamma Gaussian inverse Wishart (GGIW) distributions. A cheaper variant of the algorithm is also proposed, based on the labelled multi-Bernoulli (LMB) filter. The proposed GLMB/LMB-based algorithms are compared with an extended target version of the cardinalized probability hypothesis density (CPHD) filter, and simulation results show that the (G) LMB has improved estimation and tracking performance.

finite set statistics

CPHD filter

target tracking


extended targets

GLMB filter

Random finite sets

inverse Wishart


M. Beard

Defence Science and Technology Group

Curtin University

S. Reuter

University of Ulm

Karl Granström

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

B. T. Vo

Curtin University

B. N. Vo

Curtin University

A. Scheel

University of Ulm

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 64 7 1638-1653 7347473

Subject Categories

Signal Processing



More information

Latest update