Multi-Sensor PHD by Space Partionning: Computation of a True Reference Density Within The PHD Framework

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, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : In a previous paper, the authors proposed an extension of the Probability Hypothesis Density (PHD), a well-known method for singlesensor multi-target tracking problems in a Bayesian framework, to the multi-sensor case. The true expression of the multi-sensor data update PHD equation was constructed using finite sets statistics (FISST) derivative techniques on functionals defined onmulti-sensor observation and state space named "cross-terms". In this paper, an equivalent expression in a combinational form is provided, which allows an easier interpretation of the data update equation. Then, using the joint partitioning proposed by the authors in the previous paper, an exact multi-sensor multi-target PHD filter is efficiently propagated on a benchmark scenario involving 10 sensors and up to 10 simultaneous targets where the brute force approach would have been extremely burdensome. The availability of a true reference PHD then allows a validation of the classical iterated-corrector approximation method, albeit limited to the scope of the implemented scenario.
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Communication dans un congrès
IEEE - Signal Processing Society. Statistical Signal Processing Workshop (SSP), 2011, Jun 2011, Nice, France. IEEE - Signal Processing Society, pp.333 - 336, 2011, Statistical Signal Processing Workshop (SSP). 〈10.1109/SSP.2011.5967695〉
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Emmanuel Delande, Emmanuel Duflos, Philippe Vanheeghe, Dominique Heurguier. Multi-Sensor PHD by Space Partionning: Computation of a True Reference Density Within The PHD Framework. IEEE - Signal Processing Society. Statistical Signal Processing Workshop (SSP), 2011, Jun 2011, Nice, France. IEEE - Signal Processing Society, pp.333 - 336, 2011, Statistical Signal Processing Workshop (SSP). 〈10.1109/SSP.2011.5967695〉. 〈hal-00639710〉

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