Chen Jing, Jin Xiaojun, Hou Cong, Zhu Likai, Xu Zhaobin, Jin Zhonghe
Huanjiang Laboratory, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China.
Zhejiang Key Laboratory of Micro-Nano Satellites Research, Hangzhou 310027, China.
Sensors (Basel). 2024 Dec 14;24(24):7988. doi: 10.3390/s24247988.
Low-performing GPS receivers, often used in challenging scenarios such as attitude maneuver and attitude rotation, are frequently encountered for micro-nano satellites. To address these challenges, this paper proposes a modified robust adaptive hierarchical filtering algorithm (named IARKF). This algorithm leverages robust adaptive filtering to dynamically adjust the distribution of innovation vectors and employs a fading memory weighted method to estimate measurement noise in real time, thereby enhancing the filter's adaptability to dynamic environments. A segmented adaptive filtering strategy is introduced, allowing for flexible parameter adjustment in different dynamic scenarios. A micro-nano satellite equipped with a miniaturized dual-frequency GPS receiver is employed to demonstrate precise orbit determination capabilities. On-orbit GPS data from the satellite, collected in two specific scenarios-slow rotation and Earth-pointing stabilization-are analyzed to evaluate the proposed algorithm's ability to cope with weak GPS signals and satellite attitude instability as well as to assess the achievable orbit determination accuracy. The results show that, compared to traditional Extended Kalman Filters (EKF) and other improved filtering algorithms, the IARKF performs better in reducing post-fit residuals and improving orbit prediction accuracy, demonstrating its superior robustness. The three-axes orbit determination internal consistency precision can reach the millimeter level. This work explores a feasible approach for achieving high-performance orbit determination in micro-nano satellites.
性能不佳的全球定位系统（GPS）接收器常用于诸如姿态机动和姿态旋转等具有挑战性的场景中，在微纳卫星中经常会遇到这种情况。为应对这些挑战，本文提出了一种改进的鲁棒自适应分层滤波算法（命名为IARKF）。该算法利用鲁棒自适应滤波动态调整新息向量的分布，并采用渐消记忆加权方法实时估计测量噪声，从而增强滤波器对动态环境的适应性。引入了分段自适应滤波策略，允许在不同动态场景中灵活调整参数。使用配备了小型化双频GPS接收器的微纳卫星来展示精确轨道确定能力。对卫星在两种特定场景（缓慢旋转和对地稳定）下收集的在轨GPS数据进行分析，以评估所提出算法应对弱GPS信号和卫星姿态不稳定的能力，以及评估可实现的轨道确定精度。结果表明，与传统扩展卡尔曼滤波器（EKF）和其他改进的滤波算法相比，IARKF在减少拟合后残差和提高轨道预测精度方面表现更好，证明了其卓越的鲁棒性。三轴轨道确定内部一致性精度可达到毫米级别。这项工作探索了一种在微纳卫星中实现高性能轨道确定的可行方法。
