Multi-sensor all information fusion algorithm based on robust CKF
Xiaogong Lin · Dianji yu kongzhi xuebao · 2013
To overcome the influence of abnormal measurements and improve the performance of multi-sensor fusion,based on robust cubature Kalman filter(CKF) a multi-sensor all information fusion algorithm is proposed.By the principle of innovation covariance matching,a robust CKF was built.A data quality detection function was defined.According to the measurements,the robust CKF or normal CKF was selected as the subsystem optimal filtering algorithm.With the estimation information of multi-sensor fusion,a subsystem soft fault detection function was presented.The subsystem fault factor was introduced,and the faulty subsystem is isolated by the system reconfiguration.A multi-sensor all information fusion method was proposed based on the all information of the system.These proposed methods were applied to the vessel dynamic positioning system simulation.They were compared with normal CKF and local estimation weighted fusion algorithm.The simulation results show that the proposed robust CKF and the soft fault detection function improve the robustness and accuracy of subsystem filtering,and the all information fusion algorithm has better performance.The simulation example verifies the effectiveness of the proposed algorithms.