A Consensus-Based Distributed Calibration Algorithm for Sensor Networks

In this paper a novel distributed algorithm for blind macro-calibration in sensor networks based on consensus is proposed. The algorithm is formulated as a set of gradient-type recursions for estimating parameters of sensor calibration functions, starting from local criteria defined as weighted sums of mean square differences between the outputs of neighboring sensors. It is proved that the algorithm achieves asymptotic agreement for sensor gains and offsets in the mean square sense and with probability one. In the case of additive measurement noise, additive inter-agent communication noise and communication outages, a modification of the original algorithm is proposed. It is proved using stochastic approximation arguments that the modified algorithm achieves asymptotic consensus for sensor gains and offsets in the mean square sense and with probability one. Special attention is paid to the situation when one sensor is selected as a reference. Illustrative simulation examples are provided.