4,013 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
Transferability of Convolutional Neural Networks in Stationary Learning Tasks
Recent advances in hardware and big data acquisition have accelerated the
development of deep learning techniques. For an extended period of time,
increasing the model complexity has led to performance improvements for various
tasks. However, this trend is becoming unsustainable and there is a need for
alternative, computationally lighter methods. In this paper, we introduce a
novel framework for efficient training of convolutional neural networks (CNNs)
for large-scale spatial problems. To accomplish this we investigate the
properties of CNNs for tasks where the underlying signals are stationary. We
show that a CNN trained on small windows of such signals achieves a nearly
performance on much larger windows without retraining. This claim is supported
by our theoretical analysis, which provides a bound on the performance
degradation. Additionally, we conduct thorough experimental analysis on two
tasks: multi-target tracking and mobile infrastructure on demand. Our results
show that the CNN is able to tackle problems with many hundreds of agents after
being trained with fewer than ten. Thus, CNN architectures provide solutions to
these problems at previously computationally intractable scales.Comment: 14 pages, 7 figures, for associated code see
https://github.com/damowerko/mt
Distributed Multi-Object Tracking Under Limited Field of View Heterogeneous Sensors with Density Clustering
We consider the problem of tracking multiple, unknown, and time-varying
numbers of objects using a distributed network of heterogeneous sensors. In an
effort to derive a formulation for practical settings, we consider limited and
unknown sensor field-of-views (FoVs), sensors with limited local computational
resources and communication channel capacity. The resulting distributed
multi-object tracking algorithm involves solving an NP-hard multidimensional
assignment problem either optimally for small-size problems or sub-optimally
for general practical problems. For general problems, we propose an efficient
distributed multi-object tracking algorithm that performs track-to-track fusion
using a clustering-based analysis of the state space transformed into a density
space to mitigate the complexity of the assignment problem. The proposed
algorithm can more efficiently group local track estimates for fusion than
existing approaches. To ensure we achieve globally consistent identities for
tracks across a network of nodes as objects move between FoVs, we develop a
graph-based algorithm to achieve label consensus and minimise track
segmentation. Numerical experiments with a synthetic and a real-world
trajectory dataset demonstrate that our proposed method is significantly more
computationally efficient than state-of-the-art solutions, achieving similar
tracking accuracy and bandwidth requirements but with improved label
consistency
Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
As a fundamental information fusion approach, the arithmetic average (AA)
fusion has recently been investigated for various random finite set (RFS)
filter fusion in the context of multi-sensor multi-target tracking. It is not a
straightforward extension of the ordinary density-AA fusion to the RFS
distribution but has to preserve the form of the fusing multi-target density.
In this work, we first propose a statistical concept, probability hypothesis
density (PHD) consistency, and explain how it can be achieved by the PHD-AA
fusion and lead to more accurate and robust detection and localization of the
present targets. This forms a both theoretically sound and technically
meaningful reason for performing inter-filter PHD AA-fusion/consensus, while
preserving the form of the fusing RFS filter. Then, we derive and analyze the
proper AA fusion formulations for most existing unlabeled/labeled RFS filters
basing on the (labeled) PHD-AA/consistency. These derivations are theoretically
unified, exact, need no approximation and greatly enable heterogenous unlabeled
and labeled RFS density fusion which is separately demonstrated in two
consequent companion papers.Comment: 13 pages, 4 figures, 1 tabl
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