50,631 research outputs found
Regional variance for multi-object filtering
Recent progress in multi-object filtering has led to algorithms that compute
the first-order moment of multi-object distributions based on sensor
measurements. The number of targets in arbitrarily selected regions can be
estimated using the first-order moment. In this work, we introduce explicit
formulae for the computation of the second-order statistic on the target
number. The proposed concept of regional variance quantifies the level of
confidence on target number estimates in arbitrary regions and facilitates
information-based decisions. We provide algorithms for its computation for the
Probability Hypothesis Density (PHD) and the Cardinalized Probability
Hypothesis Density (CPHD) filters. We demonstrate the behaviour of the regional
statistics through simulation examples
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
A second-order PHD filter with mean and variance in target number
The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters
are popular solutions to the multi-target tracking problem due to their low
complexity and ability to estimate the number and states of targets in
cluttered environments. The PHD filter propagates the first-order moment (i.e.
mean) of the number of targets while the CPHD propagates the cardinality
distribution in the number of targets, albeit for a greater computational cost.
Introducing the Panjer point process, this paper proposes a second-order PHD
filter, propagating the second-order moment (i.e. variance) of the number of
targets alongside its mean. The resulting algorithm is more versatile in the
modelling choices than the PHD filter, and its computational cost is
significantly lower compared to the CPHD filter. The paper compares the three
filters in statistical simulations which demonstrate that the proposed filter
reacts more quickly to changes in the number of targets, i.e., target births
and target deaths, than the CPHD filter. In addition, a new statistic for
multi-object filters is introduced in order to study the correlation between
the estimated number of targets in different regions of the state space, and
propose a quantitative analysis of the spooky effect for the three filters
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