Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks

This paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements. Besides, since nodes in the network are constrained by communication power consumption and external interference in a non-stationary environment, the objective pursued by the node is prone to change or abnormality. The problem is worth considering in several contexts including multi-target tracking, multi-model classification and heterogeneous network segmentation. We propose a distributed adaptive clustering strategy, which is mainly composed of two procedures: normal task adaptation and the same task cluster. The task anomaly detection based on non-cooperative least-mean-squares (NC-LMS) algorithm and task switching detection based on diffusion maximum correntropy criterion (D-MCC) algorithm are provided. A series of scenarios, such as dynamic network, time-varying tasks and non-stationary (Gaussian and pulse interference) are simulated. We also discuss optimization schemes to design the NC-LMS and D-MCC weights and examine the estimate performance and clustering effects of the proposed algorithm by simulation results.