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Conference Papers Year : 2011

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

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Emmanuel Delande
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  • PersonId : 873501
Emmanuel Duflos
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  • PersonId : 844358
Philippe Vanheeghe
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  • PersonId : 838038

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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Dates and versions

hal-00639710 , version 1 (09-11-2011)

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Cite

Emmanuel Delande, Emmanuel Duflos, Philippe Vanheeghe, Dominique Heurguier. Multi-Sensor PHD by Space Partionning: Computation of a True Reference Density Within The PHD Framework. Statistical Signal Processing Workshop (SSP), 2011, IEEE - Signal Processing Society, Jun 2011, Nice, France. pp.333 - 336, ⟨10.1109/SSP.2011.5967695⟩. ⟨hal-00639710⟩
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