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首页> 外文期刊>Journal of Guidance, Control, and Dynamics >Motion and Parameter Estimation of Space Objects Using Laser-Vision Data
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Motion and Parameter Estimation of Space Objects Using Laser-Vision Data

机译：利用激光视觉数据估算空间物体的运动和参数

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摘要

Acomputationally efficient noise-adaptiveKalman filter is presented for themotion estimation and prediction of arnfree-falling tumbling satellite (target). The filter receives only noisy pose measurements from a laser-vision systemrnaboard another satellite (chaser) at a close distance in a neighboring orbit. The filter estimates the full states, all therninertia parameters of the target satellite, and the covariance of the measurement noise. A comprehensive dynamicsrnmodel that includes aspects of orbital mechanics is incorporated for accurate estimation. The discrete-time model,rnwhich involves a state-transitionmatrix and the covariance of process noise, is derived in closed form, thus renderingrnthe filter suitable for real-time implementation. The statistical characteristics of themeasurement noise is formulatedrnby a state-dependent covariance matrix. This model allows additive quaternion noise, while preserving the unit-rnnorm property of the quaternion. The convergence properties of the developed filter is demonstrated by simulationrnand experimental results. These results also demonstrate that the filter can continuously produce accurate estimatesrnof pose even when the vision system is occluded for tens of seconds.
机译：提出了一种具有计算效率的噪声自适应卡尔曼滤波器，用于对无边下降的翻滚卫星（目标）进行运动估计和预测。过滤器仅在邻近轨道的距离很近的另一颗卫星（追赶者）附近的激光视景系统中接收到嘈杂的姿态测量值。滤波器估计目标卫星的完整状态，所有温度参数以及测量噪声的协方差。包含轨道力学各方面的综合动力学模型被纳入以进行精确估计。离散状态模型以封闭形式导出，它涉及状态转换矩阵和过程噪声的协方差，因此使滤波器适合于实时实现。测量噪声的统计特性由状态相关的协方差矩阵表示。该模型允许添加四元数噪声，同时保留四元数的单位范数属性。仿真和实验结果证明了所开发滤波器的收敛特性。这些结果还表明，即使视觉系统被闭塞了数十秒钟，过滤器也可以连续产生准确的估计姿势。

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