Bayesian data fusion for distributed target detection in sensor networks

Authors:
Marco Guerriero;Lennart Svensson;Peter Willett
Affiliations:
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT;Department of Signals and Systems, Chalmers University of Technology, Göteborg, Sweden;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 4
Cited 1

Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Distributed detection and fusion in a large wireless sensor network of random size

EURASIP Journal on Wireless Communications and Networking
Distributed target detection in sensor networks using scan statistics

IEEE Transactions on Signal Processing
Performance Analysis of Distributed Detection in a Random Sensor Field

IEEE Transactions on Signal Processing

Stochastic Optimization of Sensor Placement for Diver Detection

Operations Research

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Abstract

In this correspondence, we study different approaches for Bayesian data fusion for distributed target detection in sensor networks.Due to communication and bandwidth constraints, we assume that each sensor can only transmit a local decision to the fusion center (FC), which is in charge to take the final decision about the presence of a target. The optimal Bayesian test statistic at the FC is derived in the case where both the number and locations of the sensors are known. On the other hand, if both the number and the locations of the sensors are unknown, the optimal Bayesian test statistic is computed based on the same observations that the Scan Statistic test utilizes. The performances of the different approaches are compared through simulation.