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Proceedings Paper

A distributed general multi-sensor cardinalized probability hypothesis density (CPHD) filter for sensor networks
Author(s): S. Datta Gupta; S. Nannuru; M. Coates; M. Rabbat
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Paper Abstract

We develop a distributed cardinalized probability hypothesis density (CPHD) filter that can be deployed in a sensor network to process the measurements of multiple sensors that make conditionally independent measurements. In contrast to the majority of the related work, which involves performing local filter updates and then exchanging data to fuse the local intensity functions and cardinality distributions, we strive to approximate the update step that a centralized multi-sensor CPHD filter would perform.

Paper Details

Date Published: 21 May 2015
PDF: 8 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740F (21 May 2015); doi: 10.1117/12.2177502
Show Author Affiliations
S. Datta Gupta, McGill Univ. (Canada)
S. Nannuru, McGill Univ. (Canada)
M. Coates, McGill Univ. (Canada)
M. Rabbat, McGill Univ. (Canada)


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

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