An improved adaptive Kalman filtering algorithm for advanced robot navigation system based on GPS/INS

Abstract

Navigation technology plays an important role in the designing of advanced robot. An advanced robot navigation system based on GPS/INS is modeled in this paper. According to the model, the causes of the errors in measurement equation are analyzed, concluding that HDOP (Horizontal Dilution of Precision) and VDOP (Vertical Dilution of Precision) provided by GPS receiver are the crucial factors for the change of measurement noise in the mathematical model. Based on the above conclusion, this paper proposes a novel second order fuzzy self-adaptive filter design. Choosing the differences of location and velocity information provided by GPS receiver and INS device as the inputs, this filter modifies the regulation factor based on the residual sequence statistical information and PDOP (Position Dilution of Precision) provided by GPS receiver to correct the outputs of INS device using fuzzy logic. The experimental results demonstrate that the improved adaptive Kalman filtering algorithm proposed in this paper has a strong adaptability to time-varying measurement noises, which improves precision of the advanced robot navigation.