Distributed Maximum Correntropy Cubature Information Filtering for Tracking Unmanned Aerial Vehicle
Yu Zhou, Zheng Zhen, Jinbo Huang, Chengjun Wang, Guangbo Xu, Youshi Xuchen, Bingting Zha
Abstract
Aiming at improving the performance of tracking an unmanned aerial vehicle (UAV) on the battlefield, this article focuses on the algorithm involving tracking the maneuvering target with a distributed sensor network under non-Gaussian measurement noise. A novel distributed maximum correntropy cubature information filtering (DMCCIF) based on interactive multiple model (IMM) is proposed. Taking advantage of correntropy, we design maximum correntropy cubature information filtering (MCCIF) for each node to estimate the target state under non-Gaussian measurement noise. Then, distributed information fusion based on weighted average consensus is conducted to improve the stability of the sensor network. After that, the information pair is changed so that a distributed state estimation (DSE) algorithm based on IMM is developed to increase the reliability of the maneuvering target tracking. Simulation results and comparison with other algorithms in three typical non-Gaussian measurement noise scenarios are given to evaluate the effectiveness of the proposed algorithm.