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Blind Calibration of Sensor Networks

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2 Author(s)
Balzano, L. ; Univ. of California, Los Angeles ; Nowak, R.

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 sufficient conditions for the identification of unknown sensor offsets. 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 offset 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.

Published in:

Information Processing in Sensor Networks, 2007. IPSN 2007. 6th International Symposium on

Date of Conference:

25-27 April 2007