Blind Calibration of Sensor Networks

In Proceedings of Information Processing in Sensor Networks , 10 pages , Cambridge, MA , April 2007.

NESL Technical Report #: TR-UCLA-NESL-200703-02

This paper considers the problem of blindly calibrating sensor response using routine sensor network measurements. We show that as long as the sensors slightly oversample the signals of interest, then unknown sensor gains can be perfectly recovered. Remarkably, neither a controlled stimulus nor a dense deployment is required. We also characterize necessary and sucient conditions for the identification of unknown sensor osets. Our results exploit incoherence conditions between the basis for the signals and the canonical or natural basis for the sensor measurements. Practical algorithms for gain and oset identification are proposed based on the singular value decomposition and standard least squares techniques. We investigate the robustness of the proposed algorithms to model mismatch and noise on both simulated data and on data from current sensor network deployments.

• Laura Balzano
• Robert Nowak

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• Integrity : Data Integrity in Sensor Networks

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