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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Delande, E. Duflos, E. Vanheeghe, P. Heurguier, D. |
| Copyright Year | 2011 |
| Description | Author affiliation: OPS / HAT / SPM, Thales Communication, 92704 Colombes, France (Heurguier, D.) || LAGIS FRE CNRS 3303, Ecole Centrale de Lille, 59651 Villeneuve d'Ascq, France (Delande, E.; Duflos, E.; Vanheeghe, P.) |
| Abstract | In a previous paper, the authors proposed an extension of the Probability Hypothesis Density (PHD), a well-known method for single-sensor 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 on multi-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. |
| Starting Page | 333 |
| Ending Page | 336 |
| File Size | 433096 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457705694 |
| e-ISBN | 9781457705700 |
| DOI | 10.1109/SSP.2011.5967695 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-28 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Equations Mathematical model Bayesian methods Approximation methods Current measurement Time measurement Force Multi-target tracking Probability Hypothesis Density Multi-sensor system |
| Content Type | Text |
| Resource Type | Article |
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