A New GNSS/INS Navigation Scheme Using Integrated Particle Filter and Extended Kalman Filter
Yinyin Fang, Xing He, Yong Chun Xu, Qingqing Luo, Kunkun Rui · 2023
This paper presents a new navigation scheme for the global navigation satellite system and inertial navigation system (GNSS/INS) by integrating the particle filter a nd the extended Kalman filter. In the proposed scheme, t he particle filter (PF) is used to estimate the object position and its velocity while the extended Kalman filter (EKF) is applied to predict the orientation, accelerometer error gyroscope error. In addition, the adaptive Gaussian particle swarm optimization (AGPSO) algorithm is involved into the PF to further improve the estimation accuracy. Experimental results show that the positioning and velocity errors using the proposed method are greatly reduced compared to the standard particle filter estimation method. The positioning accuracy can achieve sub-meter level, which satisfies the requirements of high-precision positioning and navigation. Finally, we analyze the sensitivity of the proposed algorithm on the number of particles, and we show that increasing the number of particles within a limited range is effective for improving the accuracy of the proposed algorithm.