Target Tracking Using a Distributed Particle-Pda Filter With Sparsity-Promoting Likelihood Consensus

Repp, Rene; Rajmic, Pavel; Meyer, Florian; Hlawatsch, Franz

doi:10.1109/ssp.2018.8450815

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Record link:

http://hdl.handle.net/20.500.12708/76540

Title:

Target Tracking Using a Distributed Particle-Pda Filter With Sparsity-Promoting Likelihood Consensus

Citation:

Repp, R., Rajmic, P., Meyer, F., & Hlawatsch, F. (2018). Target Tracking Using a Distributed Particle-Pda Filter With Sparsity-Promoting Likelihood Consensus. In 2018 IEEE Statistical Signal Processing Workshop (SSP). 2018 IEEE Statistical Signal Processing Workshop (SSP), Freiburg im Breisgau, Germany. IEEE. https://doi.org/10.1109/ssp.2018.8450815

Publisher DOI:

10.1109/ssp.2018.8450815

Publication Type:

Inproceedings - Poster Contribution

Authors:

Repp, Rene
Rajmic, Pavel
Meyer, Florian
Hlawatsch, Franz

Organisational Unit:

E389-03 - Forschungsbereich Signal Processing

Published in:

2018 IEEE Statistical Signal Processing Workshop (SSP)

Date (published):

2018

Event name:

2018 IEEE Statistical Signal Processing Workshop (SSP)

Event date:

10-Jun-2018 - 13-Jun-2018

Event place:

Freiburg im Breisgau, Germany

Number of Pages:

Publisher:

IEEE

Peer reviewed:

Yes

Keywords:

likelihood consensus; sensor network; distributed target tracking; probabilistic data association; orthogonal matching pursuit

Abstract:

We propose a distributed particle-based probabilistic data association filter (PDAF) for target tracking in the presence of clutter and missed detections. The proposed PDAF employs a new "sparsity-promoting" likelihood consensus that uses the orthogonal matching pursuit for a sparse approximation of the local likelihood functions. Simulation results demonstrate that, compared to the conventional likelihood consensus based on least-squares approximation, large savings in intersensor communication can be obtained without compromising the tracking performance.

Research Areas:

Sensor Systems: 34%
Telecommunication: 33%
Information Systems Engineering: 33%

Science Branch:

Elektrotechnik, Elektronik, Informationstechnik

Appears in Collections:

Conference Paper

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