SuperLectures.com

MULTI-SENSOR PHD: CONSTRUCTION AND IMPLEMENTATION BY SPACE PARTITIONING

Target Detection and Localisation

Full Paper at IEEE Xplore

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.

  Speech Transcript

|

  Slides

Enlarge the slide | Show all slides in a pop-up window

0:00:49

  1. slide

0:01:45

  2. slide

0:02:45

  3. slide

0:03:31

  4. slide

0:04:31

  5. slide

0:05:15

  6. slide

0:06:06

  7. slide

0:06:40

  8. slide

0:07:06

  9. slide

0:08:17

 10. slide

0:09:04

 11. slide

0:09:43

 12. slide

0:10:20

 13. slide

0:10:53

 14. slide

0:11:35

 15. slide

0:12:06

    14. slide

0:12:12

    15. slide

  Comments

Please sign in to post your comment!

  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