Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
M. Moldaschl, W. Gansterer, O. Hlinka, F. Meyer, F. Hlawatsch:
"Distributed Decorrelation in Sensor Networks with Application to Distributed Particle Filtering";
Vortrag: IEEE International Conference on Speech, Acoustics, and Signal Processing 2014 (ICASSP 2014),
Florence, Italy;
04.06.2014
- 09.06.2014; in: "IEEE ICASSP-2014",
IEEE,
(2014),
S. 6158
- 6162.
Kurzfassung englisch:
Most distributed statistical signal processing methods assume conditionally uncorrelated sensor measurements although this assumption is often not satisfied. Here, we propose a distributed algorithm for decorrelating the sensor measurements in a wireless sensor network. The algorithm employs a matrix-valued Chebyshev approximation to achieve an approximate decorrelation using only local computations and communication between neighboring sensors. We apply the algorithm to consensus-based distributed particle filtering in a target tracking problem with correlated measurement noises. Simulations show that the decorrelation yields a substantial accuracy improvement while causing only a small communication overhead.
Schlagworte:
Distributed decorrelation, wireless sensor network, Chebyshev approximation, distributed particle filtering, target tracking
Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/PubDat_228666.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.