For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.

对于配备北斗导航卫星系统/捷联惯性导航系统集成导航系统的飞机，北斗导航卫星系统信息可用于实现自动对准。然而，由于复杂的极地环境和多径效应，北斗导航卫星系统的测量噪声常常表现出非高斯分布，这将严重降低标准卡尔曼滤波器的估计精度。为了解决这个问题，本文提出了一种基于Huber估计滤波器的极坐标对准算法。考虑到极地的特殊地理条件，本文推导了北斗导航卫星系统/捷联惯性导航系统在网格框架中的综合对准系统的动力学模型和测量模型。分析了北斗导航卫星系统在极区的测量噪声特征，并模拟了重尾特征。由于在非高斯噪声下，标准卡尔曼滤波器的估计精度会大大降低，因此，结合网格导航系统和广义最大似然估计，设计了基于Huber估计的卡尔曼滤波器。仿真和实验结果表明，该算法在非高斯噪声下具有较好的鲁棒性，在极地地区有效。通过采用所提出的算法，可以提高对准过程的速度和准确性。由于在非高斯噪声下，标准卡尔曼滤波器的估计精度会大大降低，因此，结合网格导航系统和广义最大似然估计，设计了基于Huber估计的卡尔曼滤波器。仿真和实验结果表明，该算法在非高斯噪声下具有较好的鲁棒性，在极地地区有效。通过采用所提出的算法，可以提高对准过程的速度和准确性。由于在非高斯噪声下，标准卡尔曼滤波器的估计精度会大大降低，因此，结合网格导航系统和广义最大似然估计，设计了基于Huber估计的卡尔曼滤波器。仿真和实验结果表明，该算法在非高斯噪声下具有较好的鲁棒性，在极地地区有效。通过采用所提出的算法，可以提高对准过程的速度和准确性。仿真和实验结果表明，该算法在非高斯噪声下具有较好的鲁棒性，在极地地区有效。通过采用所提出的算法，可以提高对准过程的速度和准确性。仿真和实验结果表明，该算法在非高斯噪声下具有较好的鲁棒性，在极地地区有效。通过采用所提出的算法，可以提高对准过程的速度和准确性。