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Laser radar based relative navigation using improved adaptive Huber filter

机译:使用改进的自适应Huber滤波器的基于激光雷达的相对导航

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摘要

An improved adaptive Huber filter algorithm is proposed to model error and measurement noise uncertainty in this work. The adaptive algorithm for model error is obtained by using an upper bound for the state prediction covariance matrix with augment of chi-square statistical hypothesis test in case of filter deteriorated by wrong residual information. The measurement noise is estimated at each filter step by minimizing a criterion function which was original from Huber filter. A recursive algorithm is provided for solving the criterion function. The proposed adaptive filter algorithm was successfully implemented in radar navigation system for spacecraft formation flying in high earth orbits with real orbit perturbations and non-Gaussian random measurement error. Simulation results indicated that the proposed adaptive filter performed better in robustness and accuracy compared with previous adaptive algorithms.
机译:提出了一种改进的自适应Huber滤波器算法来对误差和测量噪声不确定性进行建模。通过将状态预测协方差矩阵的上限与卡方统计假设检验的增加配合使用,以防滤波器因错误的残留信息而变差,从而获得模型误差的自适应算法。通过最小化来自Huber滤波器的标准函数,可以在每个滤波器步骤中估算测量噪声。提供了一种用于求解准则函数的递归算法。所提出的自适应滤波算法已成功地在雷达导航系统中实现,用于高地球轨道航天器编队飞行并具有真实的轨道扰动和非高斯随机测量误差。仿真结果表明,与以前的自适应算法相比,所提出的自适应滤波器在鲁棒性和准确性上表现更好。

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