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.

distributed parameter estimation, Kalman filter, differential encoding, linear prediction, acoustic field

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.