Error Prediction for SINS-GPS after GPS Outage Based on Hybrid KF-UKFReportar como inadecuado

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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 239426, 9 pages -

Research Article

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China

University of Chinese Academy of Sciences, Beijing 10039, China

Received 1 July 2015; Accepted 14 September 2015

Academic Editor: Rafael Morales

Copyright © 2015 Baiqiang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The performance of MEMS-SINS-GPS integrated system degrades evidently during GPS outage due to the poor error characteristics of low-cost IMU sensors. The normal EKF is unable to estimate SINS error accurately after GPS outage owing to the large nonlinear error caused by MEMS-IMU. Aiming to solve this problem, a hybrid KF-UKF algorithm for real-time SINS-GPS integration is presented in this paper. The linear and nonlinear SINS error models are discussed, respectively. When GPS works well, we fuse SINS and GPS with KF with linear SINS error model using normal EKF. In the case of GPS outage, we implement Unscented Transform to predict SINS error covariance with nonlinear SINS error model until GPS signal recovers. In the simulation test that we designed, an evident accuracy improvement in attitude and velocity could be noticed compared to the normal EKF method after the GPS signal recovered.

Autor: Baiqiang Zhang, Hairong Chu, Tingting Sun, Hongguang Jia, Lihong Guo, and Yue Zhang



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