doi:10.16186/j.cnki.1673-9787.2020050108

Received:2020/05/31

Revised:2020/07/23

Published:2021/11/15

Adaptive traceless Kalman filtering algorithm based on GPS/INS

CAO Hongyan1, LIU Changming1, SHEN Xiaolin1, NIU Xinglong1, LI Dawei1, CHEN Yan2

1.School of Electrical and Control Eengineering,North University of China ,Taiyuan  030051 ,Shanxi,China;2.Military Representative Office of Military Equipment Department in Beijing,Taiyuan  030051 ,Shanxi,China

Abstract:Abstract ：The attitude information of the carrier is one of the most important information in the navigation parameters. In order to improve the measurement accuracy of the attitude angle ,taking INS and GPS tight integrated navigation system as the background frame ,in view of the traceless Kalman filtering algorithm is more sensitive to the error model ，the new information noise interference data preprocessing and the low effective ness of the algorithm ,an improved adaptive traceless Kalman filtering algorithm was proposed. The covariance difference between system noise and measurement noise was estimated online by adaptive window ,and the statistical characteristics closer to the actual noise were obtained to reduce the interference of data preprocessing； For the variance matrix of state prediction ,the suboptimal fading factor was introduced to reduce the calculation a-mount； In order to reduce the precision loss when the model was determined ,the statistics were introduced to the filtering process to determine the detection threshold of model uncertainty. Finally ,extended Kalman filtering ,traceless Kalman filtering and improved new filtering algorithm were used to process the data of UAV's heading trajectory. The results showed that the improved filtering fusion algorithm could improve the attitude measurement accuracy to 0. 1 °.

Key words:GPS;INS;Kalman filtering algorithm;extended Kalman filtering algorithm;trackless Kalman filtering algorithm;attitude angle

 基于GPS_INS的自适应无迹Kalman滤波算法_曹红燕 (2).pdf