An Improved Robust Fading Filtering Algorithm for the GPS/INS Integrated Navigation

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.