Adaptive filtering algorithm to GPS/INS integrated system

Abstract

GPS/INS integrated systems have applications in the self-positioning of highly dynamic targets. This paper conceives state equations and observation equations by the integrated system and GPS observation, respectively. An adaptive filtering algorithm is introduced which estimates observation noise covariance with observation data, and estimates dynamic noise covariance by use of the Sage-Husa filtering algorithm which has faster convergence speed and better stability than the conventional Kalman filtering algorithm. Finally, simulations are given to verify the effectiveness of the algorithm.