Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
On Fusers that Perform Better than Best Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feedback Control of Dynamic Systems
Feedback Control of Dynamic Systems
Detection of Signals in Noise
Dynamic Programming
Understanding packet delivery performance in dense wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Timing-sync protocol for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Convex Optimization
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Geographic gossip: efficient aggregation for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Blind calibration of sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Peer-to-peer estimation over wireless sensor networks via Lipschitz optimization
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
A distributed minimum variance estimator for sensor networks
IEEE Journal on Selected Areas in Communications
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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.