Multi-Sensor PHD: Construction and Implementation by Space Partitioning

Emmanuel Delande 1, 2 Emmanuel Duflos 1, 2 Philippe Vanheeghe 1, 2 Dominique Heurguier 3
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
3 TCS - Thales Communication & Securité
Abstract : The Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-target tracking problems in a Bayesian framework, but the extension to the multi-sensor case seems to remain a challenge. In this paper, an extension of Mahler's work to the multi-sensor case provides an expression of the true PHD multi-sensor data update equation. Then, based on the configuration of the sensors' fields of view (FOVs), a joint partitioning of both the sensors and the state space provides an equivalent yet more practical expression of the data update equation, allowing a more effective implementation in specific FOV configurations.
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Emmanuel Delande, Emmanuel Duflos, Philippe Vanheeghe, Dominique Heurguier. Multi-Sensor PHD: Construction and Implementation by Space Partitioning. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, May 2011, Prague, Czech Republic. pp.3632 - 3635, ⟨10.1109/ICASSP.2011.5947137⟩. ⟨hal-00639724⟩

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