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On the choice of reference in sensor offset calibration

Raj Thilak Rajan
Dec 2022
摘要
Sensor calibration is an indispensable feature in any networked cyberphysicalsystem. In this paper we consider a sensor network plagued with offset errorsmeasuring a rank-1 signal subspace where each sensor collects measurementsunder additive zero-mean Gaussian noise. Under varying assumptions on theunderlying noise covariance, we investigate the effect of using an arbitraryreference for estimating the sensor offsets in contrast to the mean of all theunknown sensor offsets as a reference. We show that the mean reference yieldsan efficient estimator in the mean square error sense. If the underlying noiseis homoscedastic in nature then the mean reference yields a factor 2improvement on the variance as compared any arbitrarily chosen reference withinthe network. Furthermore when the underlying noise is independent, but notidentical, we derive an expression for the improvement offered by the meanreference. We demonstrate our results using the problem of clocksynchronization in sensor networks, and present directions for future work.
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