Talks and Poster Presentations (with Proceedings-Entry):

M. Moldaschl, W. Gansterer, O. Hlinka, F. Meyer, F. Hlawatsch:
"Distributed Decorrelation in Sensor Networks with Application to Distributed Particle Filtering";
Talk: IEEE International Conference on Speech, Acoustics, and Signal Processing 2014 (ICASSP 2014), Florence, Italy; 06-04-2014 - 06-09-2014; in: "IEEE ICASSP-2014", IEEE, (2014), 6158 - 6162.

English abstract:
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.

Distributed decorrelation, wireless sensor network, Chebyshev approximation, distributed particle filtering, target tracking

Electronic version of the publication:

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