Talks and Poster Presentations (with Proceedings-Entry):
F. Xaver, G. Matz, P. Gerstoft, C. Mecklenbräuker:
"Predictive State Vector Encoding For Decentralized Field Estimation In Sensor Networks";
Poster: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012),
Kyoto, Japan;
03-25-2012
- 03-30-2012; in: "Proc. IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)",
IEEE,
(2012),
4 pages.
English abstract:
Decentralized physics-based field estimation in clustered sensor
networks requires the exchange of state vectors between
neighboring clusters. We reduce the communication overhead
between clusters by using a differential encoding of state vectors
that exploits the spatio-temporal field dependencies. This
encoding involves a Kalman prediction step that builds on
the state-space equations governing the fieldīs spatio-temporal
evolution. The Kalman step keeps the computational complexity
low. Simulation results for an acoustic field demonstrate
the approach.
Keywords:
distributed parameter estimation, Kalman filter, differential encoding, linear prediction, acoustic field
Electronic version of the publication:
http://publik.tuwien.ac.at/files/PubDat_208727.pdf
Created from the Publication Database of the Vienna University of Technology.