DISTRIBUTED GAUSSIAN PARTICLE FILTERING USING LIKELIHOOD CONSENSUS
Distributed and Collaborative Signal Processing
Presented by: Ondrej Hlinka, Author(s): 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.
Lecture Information
| Recorded: | 2011-05-27 09:50 - 10:10, Club B |
|---|---|
| Added: | 15. 6. 2011 19:36 |
| Number of views: | 54 |
| Video resolution: | 1024x576 px, 512x288 px |
| Video length: | 0:20:27 |
| Audio track: | MP3 [6.92 MB], 0:20:27 |
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