Optimization of variational Bayesian-based adaptive filter for closed-loop feedback in integrated navigation

doi:10.11947/j.AGCS.2023.20210689

Abstract

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

CLC Number:

P228

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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