Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment

Abstract

Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from factors such as sensor performance and environmental conditions. To address this problem, the Gaussian process (GP)-based maximum correntropy criterion square root cubature Kalman filter algorithm (GP-MCC-SRCKF) for EOT in non-Gaussian noise environments is proposed in this paper. The proposed method utilizes a GP to model extended objects, thereby enhancing estimation accuracy. Furthermore, weighted least squares (WLS) and MCC are incorporated to construct a cost function. The proposed method considers high-order moments of estimation error and effectively handles outliers in non-Gaussian noise environments. MCC-SRCKF improves the accuracy of object state estimation in non-Gaussian noise environments while ensuring the positive definiteness and symmetry of the error covariance matrix. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method.