Precise relative navigation is a critical enabler for distributed satellites to achieve new mission objectives impossible for a monolithic spacecraft. Carrier phase differential GPS (CDGPS) with integer ambiguity resolution (IAR) is a promising means of achieving cm-level accuracy for high-precision Rendezvous, Proximity-Operations and Docking (RPOD), In-Space Servicing, Assembly and Manufacturing (ISAM) as well as satellite formation flying and swarming. However, IAR is sensitive to received GPS signal noise, especially under severe multi-path or high thermal noise. This paper proposes a sensor-fusion approach to achieve IAR under such conditions in two coupling stages. A loose coupling stage fuses through an Extended Kalman Filter the CDGPS measurements with on-board sensor measurements such as range from cross-links, and vision-based bearing angles. A second tight-coupling stage augments the cost function of the integer weighted least-squares minimization with a soft constraint function using noise-weighted observed-minus-computed residuals from these external sensor measurements. Integer acceptance tests are empirically modified to reflect added constraints. Partial IAR is applied to graduate integer fixing. These proposed techniques are packaged into flight-capable software, with ground truths simulated by the Stanford Space Rendezvous Laboratory's S3 library using state-of-the-art force modelling with relevant sources of errors, and validated in two scenarios: (1) a high multi-path scenario involving rendezvous and docking in low Earth orbit, and (2) a high thermal noise scenario relying only on GPS side-lobe signals during proximity operations in geostationary orbit. This study demonstrates successful IAR in both cases, using the proposed sensor-fusion approach, thus demonstrating potential for high-precision state estimation under adverse signal-to-noise conditions.

翻译：精确相对导航是实现分布式卫星完成单体航天器无法实现的新任务目标的关键技术。载波相位差分GPS（CDGPS）结合整周模糊度解算（IAR）是实现厘米级精度的有效手段，可应用于高精度交会、近距操作与对接（RPOD）、在轨服务、组装与制造（ISAM）以及卫星编队飞行与集群。然而，IAR对接收到的GPS信号噪声敏感，尤其在严重多径或高热噪声条件下。本文提出一种传感器融合方法，通过两个耦合阶段实现此类条件下的IAR：松耦合阶段通过扩展卡尔曼滤波器融合CDGPS测量值与星载传感器测量值（如星间链路测距和基于视觉的方位角）；紧耦合阶段则利用来自外部传感器测量的噪声加权观测残差，通过软约束函数增强整数加权最小二乘最小化的代价函数。经验性地修改整数验收测试以反映新增约束，并采用部分IAR实现逐步整数固定。所提技术被封装为具备飞行能力的软件，利用斯坦福空间交会实验室的S3库基于最先进力建模及相关误差源模拟真实场景，在两个场景中验证：(1)低地球轨道交会对接的高多径场景，(2)仅依赖地球静止轨道近距操作中GPS旁瓣信号的高热噪声场景。研究表明，在两种情况下采用所提传感器融合方法均成功实现IAR，从而展示了在恶劣信噪比条件下实现高精度状态估计的潜力。