The estimation of relative motion between spacecraft increasingly relies on feature-matching computer vision, which feeds data into a recursive filtering algorithm. Kalman filters, although efficient in noise compensation, demand extensive tuning of system and noise models. This paper introduces FlexKalmanNet, a novel modular framework that bridges this gap by integrating a deep fully connected neural network with Kalman filter-based motion estimation algorithms. FlexKalmanNet's core innovation is its ability to learn any Kalman filter parameter directly from measurement data, coupled with the flexibility to utilize various Kalman filter variants. This is achieved through a notable design decision to outsource the sequential computation from the neural network to the Kalman filter variant, enabling a purely feedforward neural network architecture. This architecture, proficient at handling complex, nonlinear features without the dependency on recurrent network modules, captures global data patterns more effectively. Empirical evaluation using data from NASA's Astrobee simulation environment focuses on learning unknown parameters of an Extended Kalman filter for spacecraft pose and twist estimation. The results demonstrate FlexKalmanNet's rapid training convergence, high accuracy, and superior performance against manually tuned Extended Kalman filters.

翻译：航天器间相对运动的估计越来越依赖于特征匹配计算机视觉，该方法将数据输入递归滤波算法。尽管卡尔曼滤波器在噪声补偿方面高效，但其需要大量系统模型和噪声模型的调优工作。本文提出FlexKalmanNet这一新型模块化框架，通过将深度全连接神经网络与基于卡尔曼滤波的运动估计算法相结合，解决了上述问题。FlexKalmanNet的核心创新在于能够直接从测量数据中学习任意卡尔曼滤波器参数，同时具备灵活运用多种卡尔曼滤波变体的能力。这一目标通过一个显著的设计决策实现：将顺序计算任务从神经网络外包给卡尔曼滤波变体，从而构建纯前馈神经网络架构。该架构无需依赖循环网络模块即可高效处理复杂非线性特征，能更有效地捕捉全局数据模式。基于NASA Astrobee仿真环境数据的实证评估聚焦于学习扩展卡尔曼滤波器的未知参数，用于航天器位姿与旋量估计。结果表明，FlexKalmanNet具有快速训练收敛、高精度以及对人工调参扩展卡尔曼滤波器的性能优势。