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Multi-Sensor PHD by Space Partionning: Computation of a True Reference Density Within The PHD Framework
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.
Cited literature [8 references]
https://hal.inria.fr/hal-00639710
Contributor : Philippe Vanheeghe <>
Submitted on : Wednesday, November 9, 2011 - 5:59:24 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:23 PM
Long-term archiving on: : Friday, February 10, 2012 - 2:37:49 AM
Contributor : Philippe Vanheeghe <>
Submitted on : Wednesday, November 9, 2011 - 5:59:24 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:23 PM
Long-term archiving on: : Friday, February 10, 2012 - 2:37:49 AM
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SSP2011.pdf
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