This article introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model compensation, require offline tuning and a priori knowledge of the dynamical environment. Alternatively, the process noise covariance can be estimated through adaptive filtering. However, many adaptive filtering techniques are not applicable to onboard orbit determination due to computational cost or the assumption of a linear time-invariant system. Furthermore, existing adaptive filtering techniques do not constrain the process noise covariance according to the underlying continuous-time dynamical model, and there has been limited work on adaptive filtering with colored process noise. To overcome these limitations, a novel approach is developed which optimally fuses state noise compensation and dynamic model compensation with covariance matching adaptive filtering. This yields two adaptive and dynamically constrained process noise covariance estimation techniques. Unlike many adaptive filtering approaches, the new techniques accurately extrapolate over measurement outages and do not rely on ad hoc methods to ensure the process noise covariance is positive semidefinite. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative linear system and the autonomous navigation of two spacecraft orbiting an asteroid.

中文翻译：

---

用于轨道确定的自适应和动态约束过程噪声估计


本文介绍了两种新算法，可以在线准确估计离散时间卡尔曼滤波器的过程噪声协方差，以便在存在动力学模型不确定性的情况下实现稳健的轨道确定。常见的定轨过程噪声技术，例如状态噪声补偿和动态模型补偿，需要离线调谐和动态环境的先验知识。或者，可以通过自适应滤波来估计过程噪声协方差。然而，由于计算成本或线性时不变系统的假设，许多自适应滤波技术不适用于星载轨道确定。此外，现有的自适应滤波技术并不根据底层连续时间动态模型来约束过程噪声协方差，并且在使用有色过程噪声的自适应滤波方面的工作也很有限。为了克服这些限制，开发了一种新方法，该方法将状态噪声补偿和动态模型补偿与协方差匹配自适应滤波最佳地融合在一起。这产生了两种自适应且动态约束的过程噪声协方差估计技术。与许多自适应滤波方法不同，新技术可以准确地推断测量中断，并且不依赖临时方法来确保过程噪声协方差是正半定的。所提出算法的优点通过两个案例研究得到了证明：一个说明性的线性系统和两个绕小行星运行的航天器的自主导航。