A novel information fusion algorithm for GPS/INS navigation system

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) of GPS are the crucial factors for the change of measurement noise in the mathematical model. In order to decrease the navigation errors and improve the anti-interference performance, this paper proposes a novel second order fuzzy self-adaptive filter algorithm for GPS/INS navigation system. 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. Simulation experiments were conducted. The results show that the improved adaptive Kalman filtering algorithm for GPS/ INS navigation system proposed in this paper has a strong adaptability to time-varying measurement noises, which improves the navigation precision.