A simulated fusion localization algorithm with adaptive error covariance matrix for closed corridor seamless positioning

Abstract

Due to the blocking and reflection of buildings, the received Beidou satellite navigation signal (BDS) in the closed corridor will be getting weaker, which results in poor positioning accuracy or positioning failure. Introducing Ultra-Wide Band (UWB) positioning technology and establishing BDS/UWB integrated positioning system is an effective method to achieve seamless positioning. The positioning accuracy obtained by the Least Square-Kalman Filter (LS-KF) algorithm is below m level. However, the prior error covariance matrix of the KF correction process is easily contaminated, which affects the proportion of system observations and state prediction values to the optimal position estimation. A LS-KF fusion positioning algorithm based on adaptive error covariance matrix is proposed, the algorithm adjusts the state prior error covariance matrix by constructing an adaptive factor, improves the Kalman gain and the optimal estimation of the position, balances the trust of the filter estimation value to the observation value and the state prediction value of the integrated positioning system. Theoretical analysis and experimental results show that compared with LS-KF, EKF, UKF and improved LS-KF algorithm, the proposed LS-AKF algorithm not only has higher positioning accuracy, stronger anti-interference ability, but also has faster convergence speed.