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.

Date Published: 21 May 2015
PDF: 15 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740E (21 May 2015); doi: 10.1117/12.2176948

Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)