Distributed decorrelation in sensor networks with application to distributed particle filtering

Michael Moldaschl, Wilfried N. Gansterer, Ondrej Hlinka, Florian Meyer, Franz Hlawatsch

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.