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

The digital ubiquitous radar enhances the echo of small targets by long-term integration, but the tracking of unmanned aerial vehicles (UAVs) is still affected by target motion patterns, sea clutter interference, and other factors, which may result in non-Gaussian noise with significant variation. A joint optimization of kernel width and process noise covariance matrix is proposed in the Cauchy kernel-based extend Kalman filter (KF) to solve this problem. By setting the kernel width as a function of the error, an 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 experiments demonstrate that the proposed algorithm exhibits better performance. In complex noise environments, the root mean square error (RMSE) of the algorithm is reduced by 14.13% compared to extended KF (EKF).