Application of Neural Network and Improved Unscented Kalman Filter for GPS/SINS Integrated Navigation System

Di Zhao, Huaming Qian, Feng Shen

Abstract: In this article, a prediction method based on a radial basis function neural network and an improved unscented Kalman filter, is proposed to improve the accuracy of position and velocity of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system, especially in the presence of GPS signal outages. The improved unscented Kalman filter based on the adaptive theory is adopted to enhance the positioning accuracy of the GPS/SINS integrated navigation system when a GPS signal is available. A radial basis function neural network and a non-stationary time series analysis are used to predict and compensate for the positioning error of the GPS/SINS integration in the presence of GPS signal outages to improve the reliability of the navigation system. The effectiveness of the proposed method is verified by the simulation experiment and vehicle test. The simulation and test results show that the proposed method can improve the position accuracy and velocity accuracy in different GPS outage time periods.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 177 - 185
Cite this article: Zhao, Di, Qian, Huaming, Shen, Feng, "Application of Neural Network and Improved Unscented Kalman Filter for GPS/SINS Integrated Navigation System," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 177-185.
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