| HAL-Inria | Publications, software ... of Inria's scientists |
Conference papers
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 : Thursday, February 21, 2019 - 10:52:49 AM
Document(s) archivé(s) le : Friday, February 10, 2012 - 2:37:49 AM
Contributor : Philippe Vanheeghe <>
Submitted on : Wednesday, November 9, 2011 - 5:59:24 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Document(s) archivé(s) le : Friday, February 10, 2012 - 2:37:49 AM
File
SSP2011.pdf
Files produced by the author(s)