Abstract
Aiming at the problem that the accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed. Firstly, the innovation covariance matching technology is used to detect whether there is any abnormality in the system as a whole. Then the types of anomalies are distinguished by hypothesis test. Different types of anomalies have different effects on state estimation. Based on the dynamic changes of innovation, different adaptive weighting methods are adopted to correct navigation information. The simulation results show that this method can effectively improve the fault-tolerant performance of integrated navigation system in complex environment with unknown anomaly types. When both model anomalies and observation anomalies exist, the speed and position accuracy are increased by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering, which has higher navigation accuracy.
Highlights
The development of high-speed cruise aircraft puts higher demands on the accuracy and fault tolerance of navigation systems [1]
Aiming at the problem that the accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed
When both model anomalies and observation anomalies exist, the speed and position accuracy are increased by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering, which has higher navigation accuracy
Summary
The development of high-speed cruise aircraft puts higher demands on the accuracy and fault tolerance of navigation systems [1]. Different weighting matrices are used to correct the filter deviation caused by the outliers It improves the accuracy of the navigation system, but does not consider the influence of model error caused by uncertain factors on the filtering results. The adaptive factor is constructed to adjust the system noise matrix in real time to reduce the influence of uncertain dynamic model errors on navigation filtering solution. The previous references did not do too much analysis, but designed the fault-tolerant filtering algorithm by considering the innovation covariance matching error as a known type of anomaly. A SINS/BDS integrated navigation method based on classification weighted adaptive filtering is designed in this paper. According to the test results, different weighting methods are adopted to correct the filtering error caused by the uncertain factors.
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