针对舰船组合导航系统受极区极端恶劣环境影响发生外部量测故障从而降低系统精度的问题，提出基于马氏距离的极区容错联邦滤波算法。通过引入真实量测与预测量测之间的马氏距离来衡量各个子滤波器的量测质量，并通过马氏距离来构建各个子滤波器在主滤波器最优融合中对应的融合系数，降低故障子滤波器在最优融合中的比例，减少故障子滤波器对全局估计的不良影响。实验仿真表明，该算法相较于传统的联邦滤波算法，尤其在子滤波器量测故障情况下，具有更好的容错性和可靠性。
Aiming at the problem that the integrated navigation system of ships suffers from external measurement failure due to the extreme harsh environment in the polar region, which reduces the system accuracy, a polar fault-tolerant federated filtering algorithm based on Markov distance is proposed. The measurement quality of each sub-filter is measured by introducing the mahalanobis distance between the real measurement and the predicted measurement, and the corresponding fusion coefficient of each sub-filter in the optimal fusion of the main filter is constructed by using the mahalanobis distance, so as to reduce the ratio of the fault sub-filter in the optimal fusion and reduce the adverse effect of the fault sub-filter on the global estimation. The simulation results show that this algorithm has better fault-tolerance and reliability than the traditional federated filtering algorithm, especially in the case of sub-filter measurement failure.
2024,46(8): 143-148 收稿日期：2023-4-12
DOI：10.3404/j.issn.1672-7649.2024.08.026
分类号：U666.1
基金项目：国家自然科学基金资助项目（62073093，62003108）
作者简介：程建华(1977-),男,博士,教授,研究方向为惯性导航及组合导航
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