Distributed asynchronous consensus-based algorithm for blind calibration of sensor networks with autonomous gain correction

In this study, a new algorithm is proposed for distributed asynchronous consensus-based blind calibration of sensor networks with noisy communications and measurements. The algorithm consists of one autonomous recursion of the instrumental variable type for gain correction and one additional recursion of gradient type for offset correction based on the corrected gains. It is proved using asynchronous stochastic approximation arguments that the algorithm achieves asymptotic consensus with regard to both the corrected sensor gains and offsets in the mean square sense and with probability one. The algorithm is more flexible than the existing similar algorithms for blind macro-calibration and provides a superior convergence rate, especially when used in networks with one fixed reference node. Simulation results confirm the main theoretical statements.