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MULTI-SENSOR PHD: CONSTRUCTION AND IMPLEMENTATION BY SPACE PARTITIONING

Full Paper at IEEE Xplore

Target Detection and Localisation

Presented by: Emmanuel Delande, Author(s): Emmanuel Delande, CNRS, France; Emmanuel Duflos, Philippe Vanheeghe, Ecole Centrale de Lille, France; Dominique Heurguier, Thales Communications, France

The Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-target tracking problems in a Bayesian framework, but the extension to the multi-sensor case seems to remain a challenge. In this paper, an extension of Mahler’s work to the multi-sensor case provides an expression of the true PHD multi-sensor data update equation. Then, based on the configuration of the sensors’ fields of view (FOVs), a joint partitioning of both the sensors and the state space provides an equivalent yet more practical expression of the data update equation, allowing a more effective implementation in specific FOV configurations.

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  Lecture Information

Recorded: 2011-05-25 09:30 - 09:50, Club B
Added: 20. 6. 2011 00:12
Number of views: 20
Video resolution: 1024x576 px, 512x288 px
Video length: 0:19:06
Audio track: MP3 [6.45 MB], 0:19:06