Fusion of Finite-Set Distributions: Pointwise Consistency and Global Cardinality

A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). Fusion algorithms that extend covariance intersection and consensus-based approaches are such...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2019-12, Vol.55 (6), p.2759-2773
Main Authors: Uney, Murat, Houssineau, Jeremie, Delande, Emmanuel, Julier, Simon J., Clark, Daniel E.
Format: Article
Language:English
Subjects:
Algorithms
Computer Science
Consistency
Covariance
Covariance intersection (CI)
exponential mixture density (EMD)
Licenses
Message passing
Multisensor fusion
Probability density function
random finite sets (RFS)
Sensors
Signal and Image Processing
Signal processing algorithms
target tracking
Uncertainty
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Summary:A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). Fusion algorithms that extend covariance intersection and consensus-based approaches are such examples. In this paper, we analyze the variational principle underlying EMDs and show that the EMDs of finite-set distributions do not necessarily lead to consistent fusion of cardinality distributions. Indeed, we demonstrate that these inconsistencies may occur with overwhelming probability in practice, through examples with Bernoulli, Poisson, and independent identically distributed cluster processes. We prove that pointwise consistency of EMDs does not imply consistency in global cardinality and vice versa. Then, we redefine the variational problems underlying fusion and provide iterative solutions thereby establishing a framework that guarantees cardinality consistent fusion.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2019.2893083