Fusion of finite-set distributions: pointwise consistency and global cardinality
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 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.
Covariance intersection (CI), exponential mixture density (EMD), multisensor fusion, random finite sets (RFS), target tracking
2759-2773
Uney, Murat
f704f6e8-3ff8-4ec3-a670-708cf2079051
Houssineau, Jeremie
54d4df9b-ceaa-456d-b668-63c617e6894a
Delande, Emmanuel
e17b3b32-0949-4914-801e-c9386bce39a5
Julier, Simon J.
235e7de9-827e-4fb8-bc04-46e93136ac43
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
1 December 2019
Uney, Murat
f704f6e8-3ff8-4ec3-a670-708cf2079051
Houssineau, Jeremie
54d4df9b-ceaa-456d-b668-63c617e6894a
Delande, Emmanuel
e17b3b32-0949-4914-801e-c9386bce39a5
Julier, Simon J.
235e7de9-827e-4fb8-bc04-46e93136ac43
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Uney, Murat, Houssineau, Jeremie, Delande, Emmanuel, Julier, Simon J. and Clark, Daniel E.
(2019)
Fusion of finite-set distributions: pointwise consistency and global cardinality.
IEEE Transactions on Aerospace and Electronic Systems, 55 (6), , [8613927].
(doi:10.1109/TAES.2019.2893083).
Abstract
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.
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e-pub ahead of print date: 16 January 2019
Published date: 1 December 2019
Additional Information:
Funding Information:
This work was supported in part by the Engineering and Physical Sciences Research Council under Grant EP/K014277/1 and in part by the MOD University Defence Research Collaboration in Signal Processing.
Publisher Copyright:
© 1965-2011 IEEE.
Keywords:
Covariance intersection (CI), exponential mixture density (EMD), multisensor fusion, random finite sets (RFS), target tracking
Identifiers
Local EPrints ID: 475501
URI: http://eprints.soton.ac.uk/id/eprint/475501
ISSN: 0018-9251
PURE UUID: 0c33b931-6b46-4140-b305-4008d9486ce1
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Date deposited: 20 Mar 2023 17:45
Last modified: 17 Mar 2024 13:11
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Contributors
Author:
Murat Uney
Author:
Jeremie Houssineau
Author:
Emmanuel Delande
Author:
Simon J. Julier
Author:
Daniel E. Clark
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