SuperLectures.com

MULTI-SENSOR PHD: CONSTRUCTION AND IMPLEMENTATION BY SPACE PARTITIONING

Full Paper at IEEE Xplore

Target Detection and Localisation

Přednášející: Emmanuel Delande, Autoři: 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.


  Přepis řeči

|

  Slajdy

Zvětšit slajd | Zobrazit všechny slajdy

0:00:49

  1. slajd

0:01:45

  2. slajd

0:02:45

  3. slajd

0:03:31

  4. slajd

0:04:31

  5. slajd

0:05:15

  6. slajd

0:06:06

  7. slajd

0:06:40

  8. slajd

0:07:06

  9. slajd

0:08:17

 10. slajd

0:09:04

 11. slajd

0:09:43

 12. slajd

0:10:20

 13. slajd

0:10:53

 14. slajd

0:11:35

 15. slajd

0:12:06

    14. slajd

0:12:12

    15. slajd

  Komentáře

Please sign in to post your comment!

  Informace o přednášce

Nahráno: 2011-05-25 09:30 - 09:50, Club B
Přidáno: 20. 6. 2011 00:12
Počet zhlédnutí: 20
Rozlišení videa: 1024x576 px, 512x288 px
Délka videa: 0:19:06
Audio stopa: MP3 [6.45 MB], 0:19:06