A dynamic Bayesian nonparametric model for blind calibration of sensor networks

Abstract

In the sensor network blind calibration problem, the gains and offsets of sensors are estimated from noisy observations of unknown underlying signals. This is in general a non-identifiable problem, unless restrictive assumptions on the signal subspace or sensor observations are imposed. To overcome these assumptions, we propose a dynamic Bayesian nonparametric model. We show that if the unknown underlying signals follow the first-order auto-regressive process, then the sensor gains and offsets are identifiable. Furthermore, our model allows sensors to form clusters, where each cluster observes the same underlying signal. The clusters are however not known a priori, and are learned through the sensor data. We present a block Gibbs sampling inference method based on the forward filtering backward sampling algorithm. Simulation results suggest that our approach can estimate the sensor gains and offsets with good accuracy, and performs better than methods that first perform clustering and then blind calibration.