顾及组合导航闭环反馈的变分贝叶斯自适应滤波优化算法

doi:10.11947/j.AGCS.2023.20210689

摘要/Abstract

摘要： 多传感器组合的方式可以较好地应对全球导航卫星系统信号被遮挡、干扰等情况下的导航定位问题,滤波方法是导航定位中将多源数据融合最常使用的方法之一。在滤波过程中,组合导航的系统噪声和量测噪声无法实时精确地测定,常通过自适应滤波的方法进行时间更新和量测更新的平衡解算。贝叶斯自适应滤波方法在很多时候具有较好的效果,但是和其他的自适应滤波方法一样,该类方法都需要进行自适应因子的选取。本文根据组合导航对于实时性要求及其闭环反馈的特殊性,在变分贝叶斯自适应滤波的基础上进行了算法的优化,给出了一种调节因子的动态计算方法,并以GNSS和惯性导航系统组合系统为例,通过模拟和实测试验进行验证。试验结果表明,本文算法不需要通过迭代计算的方法就可以获取高精度组合结果,提升了计算效率;对于真实的动态场景中,本文算法的调节因子动态自适应确定,结果更具有优越性。

关键词: 变分贝叶斯, 自适应滤波, 组合导航, 闭环反馈

Abstract: Multi-sensor combination can deal with navigation and positioning problems in the case of GNSS signals being blocked and interfered. Filtering method is one of the most commonly adopted methods for multi-source data fusion in navigation and positioning. In the filtering process, the system noise and measurement noise of integrated navigation in the dynamic process cannot be accurately determined, so the adaptive filtering method is always used to balance the time update and measurement update. The Bayesian adaptive filtering method has good effect in many occasions, but it needs to select the adaptive factor just like other adaptive filtering methods. Based on the real-time requirement of integrated navigation and the particularity of closed-loop feedback, the Bayesian adaptive filtering is adopted and optimized in this paper and the dynamic calculation of adjusting factor is presented. Finally, the combination of GNSS and inertial navigation system (INS) is taken as an example to verify the effectiveness by simulation and actual experiment. The experimental results show that the algorithm proposed in this paper can obtain high-precision results without iterative calculation, which improves the computational efficiency. For real dynamic scenes, the result is more advantageous due to the dynamic adaptive adjustment factors.

Key words: variational Bayesian, adaptive filter, integrated navigation, closed-loop feedback

中图分类号:

P228

李增科, 孙耀文, 陈昭冰, 赵龙, 高井祥. 顾及组合导航闭环反馈的变分贝叶斯自适应滤波优化算法[J]. 测绘学报, 2023, 52(5): 725-737.

LI Zengke, SUN Yaowen, CHEN Zhaobing, ZHAO Long, GAO Jingxiang. Optimization of variational Bayesian-based adaptive filter for closed-loop feedback in integrated navigation[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(5): 725-737.

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