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首页> 外文期刊>Journal of guidance, control, and dynamics >Nonlinear Kalman Filtering for Improved Angles-Only Navigation Using Relative Orbital Elements
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Nonlinear Kalman Filtering for Improved Angles-Only Navigation Using Relative Orbital Elements

机译：非线性卡尔曼滤波用于使用相对轨道元素改进仅角度导航

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摘要

This paper addresses the design and validation of an accurate estimation architecture for autonomous angles-only navigation in orbits of arbitrary eccentricity. The proposed filtering strategy overcomes the major deficiencies of existing approaches in the literature, which mainly focus on applications in near-circular orbits and generally suffer from poor dynamical observability due to linearizing the filter dynamics and measurement models. Consequently, traditional angles-only navigation solutions require conducting known orbital maneuvers to reconcile the ambiguous range. In contrast, the algorithms developed in this work enable accurate maneuver-free reconstruction of the relative orbital motion. This is done through the full exploitation of nonlinearities in the measurement model using the unscented Kalman filter to improve dynamical observability and filter performance. The filter estimates mean relative orbit elements, adopting a state transition matrix subject to secular and long-period J_2 perturbation effects to decouple observable from unobservable parameters. The complete state is then reconciled with the angle measurements in the measurement model through a nonlinear transformation that includes the conversion from mean to osculating orbital elements. The resulting linear dynamics model is supplemented by either first-order Gauss-Markov processes (that is, differential empirical accelerations) or by a covariance-matcbing approach to online adaptive process noise tuning to increase performance at minimal computational complexity. Finally, the estimation architecture is completed by a novel deterministic algorithm for batch initial relative orbit determination to accurately initialize the sequential filter.
机译：本文讨论了在任意偏心率轨道上仅用于自主角度导航的精确估算体系结构的设计和验证。所提出的滤波策略克服了文献中现有方法的主要缺陷，该缺陷主要集中在近圆形轨道上的应用，并且由于使滤波器动力学和测量模型线性化而通常具有较差的动态可观测性。因此，传统的仅角度导航解决方案需要进行已知的轨道操纵以协调模糊范围。相反，在这项工作中开发的算法可以实现相对轨道运动的精确无操纵重构。这是通过使用无味卡尔曼滤波器充分利用测量模型中的非线性来改善动态可观察性和滤波器性能来完成的。过滤器估计平均相对轨道元素，采用受长期和长期J_2扰动影响的状态转换矩阵，将可观测参数与不可观测参数解耦。然后，通过非线性转换将完整状态与测量模型中的角度测量值进行协调，该非线性转换包括从均值到振荡轨道元素的转换。生成的线性动力学模型通过一阶高斯-马尔可夫过程（即微分经验加速度）或协方差匹配方法进行在线自适应过程噪声调整，从而以最小的计算复杂度来提高性能，从而得到了补充。最后，通过新颖的确定性算法来完成估算体系结构，该算法用于批处理初始相对轨道确定，以准确地初始化顺序滤波器。

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  • 来源
    《Journal of guidance, control, and dynamics》 |2017年第9期|2183-2200|共18页
  • 作者

    Joshua Sullivan; Simone DAmico;

  • 作者单位

    Space Rendezvous Laboratory, Stanford University, Stanford, California 94305-4035;

    Space Rendezvous Laboratory, Stanford University, Stanford, California 94305-4035;

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  • 正文语种 eng
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