3 research outputs found
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Multistatic Bayesian extended target tracking
To track an extended target presents challenges because the hypothesis of one target means one detection is not valid. Several approaches to extended target tracking (ETT) have been found promising, and in particular those involving random matrices have demonstrated their appeal. When targets are extended and the data is multistatic the issues are compounded; the random matrix model has continued appeal and offers a way to avoid enumerative data association. In this paper, a bistatic Bayesian ETT approach integrated into the random matrix framework is proposed. Furthermore, a closed-form solution for fusing multistatic radar system data into the same framework is presented. The proposed approaches are tested on both simulated data and real data