Improved UKF integrated navigation algorithm based on BP neural network

• Main Authors: Yu Geng, Fang Hongtao
• Format: Article
• Language: zho
• Published: National Computer System Engineering Research Institute of China 2019-04-01
• Series: Dianzi Jishu Yingyong
• Subjects: ILS; GLS; INS; federated Kalman filtering; back propagation neural network
• Online Access: http://www.chinaaet.com/article/3000100115

In the process of approaching the landing, the instrument landing system(ILS) is vulnerable to the external environment and airspace, resulting in the problem of reduced navigation accuracy. This paper proposes an inertial navigation system(INS) and GBAS landing system(GLS). The improved combined navigation algorithm uses the difference between the output position information of the integrated navigation system as the measured value of the improved unscented Kalman filter(UKF) of the BP neural network, and obtains the global optimality estimated value of the system through the optimal weighting method. Compared with the traditional federated filtering algorithm, the proposed algorithm can effectively reduce the measurement noise, reduce the error when the aircraft approaches the landing, and improve the navigation accuracy.