In this work, we present a robust sensor fusion system for exploratory data collection, exploiting the spatial redundancy in sensor networks. Unlike prior work, our system design criteria considers a heterogeneous correlated noise model and packet loss, but no prior knowledge of signal characteristics. The former two assumptions are both common signal degradation sources in sensor networks, while the latter allows exploratory data collection of unknown signals. Through both a numerical example and an experimental study on a large military site, we show that our proposed system reduces the noise in an unknown signal by 58.2% better than a comparable algorithm. © 2010 Springer-Verlag.
CITATION STYLE
Kim, Y., Schmid, T., & Srivastava, M. B. (2010). Design and implementation of a robust sensor data fusion system for unknown signals. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6131 LNCS, pp. 77–91). https://doi.org/10.1007/978-3-642-13651-1_6
Mendeley helps you to discover research relevant for your work.