A Novel Hybrid Fusion Algorithm for Low-Cost GPS/INS Integrated Navigation System During GPS Outages
Dengao Li, Xuan Jia, Jumin Zhao
Abstract
It is the main challenge for Global Positioning System (GPS)/Inertial Navigation System (INS) to achieve reliable and low-cost positioning solutions during GPS outages. A new GPS/INS hybrid method is proposed to bridge GPS outages. Firstly, a data pre-processing algorithm based on empirical mode decomposition (EMD) for wavelet de-noising is developed to reduce the uncertain noise of IMU raw measurements and provide accurate information for subsequent GPS/INS data fusion and training samples. Then, the interactive multi-model extended Kalman filter(IMM-EKF) algorithm is proposed to improve the robustness of Kalman filter output and the accuracy of model training target output. Finally, a new intelligent structure of GPS/INS based on Extreme Learning Machine (ELM) is proposed. When the GPS is available, the IMM-EKF is used to fuse the GPS and de-noised INS data, and the de-noised INS data and the outputs of IMM-EKF are used to train the ELM. During GPS outages, the ELM is used to predict and correct the INS position error. In order to evaluate the effectiveness of the proposed method, 3 tests were performed in the actual field test. The comparison results show that the proposed fusion method can significantly improve the accuracy and reliability of positioning during GPS outages.