Guo Yang, Pang Qinghao, Yin Xianlong, Shi Xueshu, Zhao Zhengxu, Sun Jian, Wang Jinsheng
Shandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, China.
Key Laboratory of Intelligent Space TT&O (Space Engineering University), Ministry of Education, Beijing 101416, China.
Sensors (Basel). 2025 Apr 17;25(8):2527. doi: 10.3390/s25082527.
As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data and dynamic noise interference, and the traditional Extended Kalman Filter (EKF) struggles to meet the accuracy and robustness requirements in complex orbital environments. To address these challenges, this paper proposes a Bayesian Adaptive Extended Kalman Filter (BAEKF), which synergistically optimizes track determination through dynamic noise covariance adjustment and Bayesian a posteriori probability correction. Experiments demonstrate that the average root mean square error (RMSE) of BAEKF is reduced by 34.7% compared to the traditional EKF, effectively addressing EKF's accuracy and stability issues in nonlinear systems. The RMSE values of UKF, RBFNN, and GPR also show improvement, providing a reliable solution for high-precision orbital determination using optical observation.
随着低地球轨道（LEO）上卫星数量和空间碎片数量的增加，高精度轨道确定对于确保航天器的安全运行和维持空间态势感知至关重要。然而，地基光学观测受到有限的弧段角度数据和动态噪声干扰的限制，传统的扩展卡尔曼滤波器（EKF）在复杂的轨道环境中难以满足精度和鲁棒性要求。为应对这些挑战，本文提出了一种贝叶斯自适应扩展卡尔曼滤波器（BAEKF），它通过动态噪声协方差调整和贝叶斯后验概率校正协同优化轨道确定。实验表明，与传统EKF相比，BAEKF的平均均方根误差（RMSE）降低了34.7%，有效解决了EKF在非线性系统中的精度和稳定性问题。无迹卡尔曼滤波器（UKF）、径向基函数神经网络（RBFNN）和高斯过程回归（GPR）的RMSE值也有所改善，为利用光学观测进行高精度轨道确定提供了可靠的解决方案。
