Cauchy Kernel-Based AEKF for UAV Target Tracking via Digital Ubiquitous Radar Under the Sea–Air Background

作者：Xinzhe Ye, Wei Xue, Xiaolong Chen, Yanmin Zhang, Xinghai Wang, Jian Guan · 发表于：IEEE Geoscience and Remote Sensing Letters · 年份：2024 · DOI：10.1109/lgrs.2024.3402687 · 被引用次数：8 · 研究领域：Target Tracking and Data Fusion in Sensor Networks、Radar Systems and Signal Processing、Inertial Sensor and Navigation

The digital ubiquitous radar enhance the echo of small target by long-term integration, but the tracking of UAV is still affected by target motion patterns, sea clutter interference and other factors, which may resulting in Non Gaussian noise with significant variation. A joint optimization of kernel width and process noise covariance matrix is proposed in Cauchy kernel-based extend Kalman filter to solve this problem. By setting the kernel width as a function of the error, iteration of the kernel width is added to the algorithm so that the error decays the fastest along the rising gradient, and then the process noise covariance matrix is corrected to serve as the basis for the estimation of the next moment.Simulation and tracking experiment demonstrate that the proposed algorithm exhibits better performance.In complex noise environments, the RMSE of the algorithm is reduced by 14.13% compared to EKF.