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Distributed fusion of multitarget densities and consensus PHD/CPHD filters

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Authors
Battistelli, G.
Chisci, L.
Fantacci, C.
Farina, A.
Mahler, Ronald
Date
2015
Type
Conference Paper

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Battistelli, G. and Chisci, L. and Fantacci, C. and Farina, A. and Mahler, R. 2015. Distributed fusion of multitarget densities and consensus PHD/CPHD filters.
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Proceedings of SPIE - The International Society for Optical Engineering
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Department of Electrical and Computer Engineering
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Abstract

© 2015 SPIE. The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown how consensus can effectively be adopted in order to perform in a scalable way the KLA fusion of multitarget densities over a peer-to-peer (i.e. without coordination center) sensor network. When the multitarget information available in each node can be expressed as a (possibly Cardinalized) Probability Hypothesis Density (PHD), application of the proposed KLA fusion rule leads to a consensus (C)PHD filter which can be successfully exploited for distributed multitarget tracking over a peer-to-peer sensor network.