Abstract
A multiextended-target tracker based on the extended target Gaussian-mixture probability hypothesis density (ET-GMPHD) filter, which can provide the tracks of the extended targets, is proposed to maintain the track continuity for the extended targets. To identify the extended targets, each individual Gaussian term of the mixture representing the posterior intensity function will be assigned a label, which is evolved through time. Then a track management scheme, including track initiation, track confirmation, track propagation, and termination, is developed to form the tracks for the extended targets. Furthermore, to improve the performance of the extended target tracker we also propose a mixture partitioning algorithm for resolving the identities of the extended targets in close proximity. The simulation results show that our proposed tracker achieves the less error of the position estimates and decreases the probability of incorrect label assignments from 0.6 to 0.25.
Highlights
In general target tracking applications, it is assumed that each target produces at most one measurement per time step
The target kinematical states are modeled by a Gaussian distribution, and the ellipsoidal target extension is modeled by a random matrix which follows the inverse Wishart distribution
We propose a multiextended-target tracker based on the original ET-GMPHD filter [21, 22], which provides the state estimates of targets at each time step and the association of state estimates to targets over time
Summary
In general target tracking applications, it is assumed that each target produces at most one measurement per time step. The multiple hypothesis tracker and assignment algorithms are applied to the particle PHD filter to form the tracks of targets in [24] and [25, 26], respectively. Clark et al introduced a technique to identify the state estimates of objects in the GMPHD filter [29] This method was successfully applied in sonar image tracking [30]. Panta et al [32] proposed a GMPHD filter-based multitarget tracker, which provided track labels and the association amongst state estimates of targets over time. (i) To obtain the temporal association for the state estimates of individual extended targets, we assign the labels to individual Gaussian terms and develop a method of the label evolution through time.
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