This talk considers the problem of blindly calibrating sensor networks using routine measurements. I will 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. I will also characterize necessary and sufficient conditions for the identification of unknown sensor offsets. These 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 based on the singular value decomposition and standard least squares techniques will be presented. The robustness of the proposed algorithms to model mismatch and noise are investigated with both simulated data and using data from current sensor network deployments.
Audio (MP3 File, Podcast Ready)
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