DISTRIBUTED GAUSSIAN PARTICLE FILTERING USING LIKELIHOOD CONSENSUS
Distributed and Collaborative Signal Processing
Přednášející: Ondrej Hlinka, Autoři: Ondrej Hlinka, Ondrej Sluciak, Franz Hlawatsch, Vienna University of Technology, Austria; Petar Djuric, Stony Brook University, United States; Markus Rupp, Vienna University of Technology, Austria
We propose a distributed implementation of the Gaussian particle filter (GPF) for use in a wireless sensor network. Each sensor runs a local GPF that computes a global state estimate. The updating of the particle weights at each sensor uses the joint likelihood function, which is calculated in a distributed way, using only local communications, via the recently proposed likelihood consensus scheme. A significant reduction of the number of particles can be achieved by means of another consensus algorithm. The performance of the proposed distributed GPF is demonstrated for a target tracking problem.
Informace o přednášce
| Nahráno: | 2011-05-27 09:50 - 10:10, Club B |
|---|---|
| Přidáno: | 15. 6. 2011 19:36 |
| Počet zhlédnutí: | 54 |
| Rozlišení videa: | 1024x576 px, 512x288 px |
| Délka videa: | 0:20:27 |
| Audio stopa: | MP3 [6.92 MB], 0:20:27 |
Příbuzné přednášky
0:23:00
COOPERATIVE PREY HERDING BASED ON DIFFUSION ADAPTATION
Distributed and Collaborative Signal Processing
Přidáno: 16. 6. 2011 00:50
0:18:42
CONTENT PREFERENCE ESTIMATION IN ONLINE SOCIAL NETWORKS: MESSAGE PASSING VERSUS SPARSE RECONSTRUCTION ON GRAPHS
Distributed and Collaborative Signal Processing
Přidáno: 15. 6. 2011 06:46
Komentáře