Author:
Zhao Wenjie,Fang Zhou,Li Ping
Abstract
This paper reports on a new navigation algorithm for fixed-wing Unmanned Aerial Vehicles (UAVs) to bridge Global Position System (GPS) outages, based on a common navigation system configuration. The ground velocity is obtained from wind-compensated airspeed, and a centripetal force model is introduced to estimate the motion acceleration. Compensated by this acceleration, the gravity vector can be extracted from the accelerometer measurement. Finally, fusing the information of the ground velocity, magnetic heading, barometric height, and gravity vector, the Integrated Navigation System (INS) is reconstructed, and an Extended Kalman Filter (EKF) is used to estimate INS errors. Hardware-in-loop simulation results show that compared with INS-only solutions, the proposed method effectively resists long-term drift of INS errors and significantly improves the accuracy for dynamic navigation during GPS outages.
Publisher
Cambridge University Press (CUP)
Subject
Ocean Engineering,Oceanography
Reference23 articles.
1. Yuan X. , Yu J.X. and Chen Z. (1993). Navigation System. 1st Edition. Aviation Industry Press, Beijing. (in Chinese).
2. Shi H. , Zhu J.H. and Sun Z.Q. (2005). INS/GPS Integrated System State Estimation Based on Hopfield Neural Network. IEEE Proceeding in Neural Networks and Brain, Beijing, China, 975–979.
3. Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors
4. Pang C.P. and Liu Z.Z. (2009). Bridging GPS Outages of Tightly Coupled GPS/SINS Based on the Adaptive Track Fusion Using RBF neural network. IEEE International Symposium on Industrial Electronics, Seoul, Korea, 971–976.
5. Fu M.Y. , Deng Z.H. and Zhang J.W. (2003). Kalman Filtering Theory and Its Application in the Navigation System. 1st Edition. Science Press, Beijing. (in Chinese).
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