Gossip-Based Algorithm for Joint Signature Estimation and Node Calibration in Sensor Networks

We consider the problem of joint sensor calibration and target signature estimation using distributed measurements over a large-scale sensor network. Specifically, we develop a new Distributed Signature Learning and Node Calibration algorithm (D-SLANC) which simultaneously estimates source signal´s signature and estimates calibration parameters local to each sensor node. We model the sensor network as a connected graph and make use of the gossip-based distributed consensus to update the estimates at each iteration of the algorithm. The algorithm is robust to link and node failures. We prove convergence of the algorithm to the centralized data pooling solution. We compare performance with the Cramér-Rao bound, and study the scaling performance of both the CR bound and the D-SLANC algorithm. The algorithm has application to classification, target signature estimation, and blind calibration in large sensor networks.