Distributed Maximum Correntropy Estimation Under Energy Harvesting Constraints Over Sensor Networks

This paper studies the distributed maximum correntropy estimation issue for nonlinear time-varying systems over energy-harvesting-constrained sensor networks in non-Gaussian noises. The modeled communication scenario is that sensors equipped with energy harvesters select some neighbors for data transmission based on their energy level and the neighbors' priorities. The expectations of a sensor transmitting data to its neighbors can be obtained by recursively computing its energy probability distribution. A cost function based on the maximum correntropy criterion (MCC) rather than the conventional minimum covariance is the optimization index to improve the estimation effect in non-Gaussian environments. The optimal estimator gain and an upper bound of the estimation error covariance are calculated using the MCC and a fixed-point iteration scheme. A sufficient condition is derived to guarantee the convergence of the fixed-point algorithm. The proposed new energy-based maximum correntropy estimator utilizes only local information and information from neighbors, thereby enabling a distributed framework. Finally, a numerical example demonstrates the effectiveness of the estimation design.