An Improved Robust Fading Filtering Algorithm for the GPS/INS Integrated Navigation
As an estimator of the state space, Kalman filter has been widely applied in the field of dynamic navigation and positioning. However, the divergence will be likely to happen when significant errors of the model exist. Thus, the fading factor is introduce
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Abstract As an estimator of the state space, Kalman filter has been widely applied in the field of dynamic navigation and positioning. However, the divergence will be likely to happen when significant errors of the model exist. Thus, the fading factor is introduced to control the influences of the state model errors. In order to improve the performance of the filter, the multiple fading factors are adopted to address the problem that a single fading factor fails to control the interferences of all model errors. By minimizing the estimation error in the worst case, the H-infinity filter can be adopted to address the uncertainties under different conditions. Nevertheless, the H-infinity filter cannot resist the influences of outliers. The robust estimation method is thus integrated with the H-infinity filtering algorithm to improve the stability of the filter furtherly. Data of the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated navigation system are collected with GPS receivers and Inertial Measurement Units (IMU) under actual conditions. Experiments using different filtering algorithms together with the contrastive analysis are performed with the collected data. Results demonstrate that the proposed filtering algorithm shows better stability. Both the filter divergence and the influences of the outliers are controlled effectively with the proposed filtering algorithm, and precision of the filtering results are improved simultaneously. Keywords Fading filter estimation
H-infinity filter
Integrated navigation
Robust
C. Jiang S. Zhang Q. Zhang (&) School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China e-mail: [email protected] C. Jiang S. Zhang Q. Zhang Collaborative Innovation Center for Resource Utilization and Ecological Restoration of Old Industrial Base, China University of Mining and Technology, Xuzhou 221116, China © Springer Nature Singapore Pte Ltd. 2017 J. Sun et al. (eds.), China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume II, Lecture Notes in Electrical Engineering 438, DOI 10.1007/978-981-10-4591-2_30
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1 Introduction The required accuracy of the navigation instrument increases along with the development of the navigation techniques. It has become an important aspect by integrating the navigation techniques to improve the instruments’ performance. GPS is characterized by all-whether measurement and high precision, and INS is a self-navigation system which indicates that no signals of the INS is sent and received. The integration of GPS and INS has been widely studied and applied. As an optimal estimation method, the Kalman filter has found extremely wide applications. Kalman filter is proposed based on the H2 norm, and the performance is closely related to the accuracy of the system model and the statistical information of the noises. It may cause a filter divergence when significant errors of the model exist [1]. Based on the fading memory, an optimal fa
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