364 research outputs found
Robust Distributed Fusion with Labeled Random Finite Sets
This paper considers the problem of the distributed fusion of multi-object
posteriors in the labeled random finite set filtering framework, using
Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI
fusion with labeled multi-object densities strongly relies on label
consistencies between local multi-object posteriors at different sensor nodes,
and hence suffers from a severe performance degradation when perfect label
consistencies are violated. Moreover, we mathematically analyze this phenomenon
from the perspective of Principle of Minimum Discrimination Information and the
so called yes-object probability. Inspired by the analysis, we propose a novel
and general solution for the distributed fusion with labeled multi-object
densities that is robust to label inconsistencies between sensors.
Specifically, the labeled multi-object posteriors are firstly marginalized to
their unlabeled posteriors which are then fused using GCI method. We also
introduce a principled method to construct the labeled fused density and
produce tracks formally. Based on the developed theoretical framework, we
present tractable algorithms for the family of generalized labeled
multi-Bernoulli (GLMB) filters including -GLMB, marginalized
-GLMB and labeled multi-Bernoulli filters. The robustness and
efficiency of the proposed distributed fusion algorithm are demonstrated in
challenging tracking scenarios via numerical experiments.Comment: 17pages, 23 figure
Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
A decentralized Poisson multi-Bernoulli filter is proposed to track multiple
vehicles using multiple high-resolution sensors. Independent filters estimate
the vehicles' presence, state, and shape using a Gaussian process extent model;
a decentralized filter is realized through fusion of the filters posterior
densities. An efficient implementation is achieved by parametric state
representation, utilization of single hypothesis tracks, and fusion of vehicle
information based on a fusion mapping. Numerical results demonstrate the
performance.Comment: 14 pages, 5 figure
Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter
Urban intersections put high demands on fully automated vehicles, in
particular, if occlusion occurs. In order to resolve such and support vehicles
in unclear situations, a popular approach is the utilization of additional
information from infrastructure-based sensing systems. However, a widespread
use of such systems is circumvented by their complexity and thus, high costs.
Within this paper, a generic interface is proposed, which enables a huge
variety of sensors to be connected. The sensors are only required to measure
very few features of the objects, if multiple distributed sensors with
different viewing directions are available. Furthermore, a Labeled
Multi-Bernoulli (LMB) filter is presented, which can not only handle such
measurements, but also infers missing object information about the objects'
extents. The approach is evaluated on simulations and demonstrated on a
real-world infrastructure setup
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
Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion
This paper proposes a heterogenous density fusion approach to scalable
multisensor multitarget tracking where the inter-connected sensors run
different types of random finite set (RFS) filters according to their
respective capacity and need. These diverse RFS filters result in heterogenous
multitarget densities that are to be fused with each other in a proper means
for more robust and accurate detection and localization of the targets. Our
approach is based on Gaussian mixture implementations where the local Gaussian
components (L-GCs) are revised for PHD consensus, i.e., the corresponding
unlabeled probability hypothesis densities (PHDs) of each filter best fit their
average regardless of the specific type of the local densities. To this end, a
computationally efficient, coordinate descent approach is proposed which only
revises the weights of the L-GCs, keeping the other parameters unchanged. In
particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB)
filters are considered. Simulations have demonstrated the effectiveness of the
proposed approach for both homogeneous and heterogenous fusion of the
PHD-MB-LMB filters in different configurations.Comment: 11 pages, 14 figures. IEEE Transactions on Aerospace and Electronics
Systems, 202
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