Open AccessAn Improved Adaptive Extended Kalman Filter Algorithm of SINS/GPS Loosely-Coupled Integrated Navigation System
Author(s) -
Yuyan Wang,
Xiuyun Meng,
Jilu Liu
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.27.22488
Subject(s) - kalman filter , fast kalman filter , invariant extended kalman filter , alpha beta filter , control theory (sociology) , gps/ins , computer science , ensemble kalman filter , extended kalman filter , adaptive filter , algorithm , noise (video) , kernel adaptive filter , global positioning system , covariance intersection , filter (signal processing) , filter design , artificial intelligence , computer vision , assisted gps , moving horizon estimation , telecommunications , control (management) , image (mathematics)
The Kalman Filter algorithm usually cannot estimate noise statistics in real-time, in order to deal with this issue, a new kind of improved Adaptive Extended Kalman Filter algorithm is proposed. Based on residual sequence, this algorithm mainly improves the adaptive estimator of the filter algorithm, which can estimate measurement noise in real-time. Furthermore, this new filter algorithm is applied to a SINS/GPS loosely-coupled integrated navigation system, which can automatically adjust the covariance matrix of measurement noise as noise varies in the system. Finally, the original Extended Kalman Filter and the improved Adaptive Extended Kalman Filter are applied respectively to simulate for the SINS/GPS loosely-coupled model. Tests demonstrate that, the improved Adaptive Extended Kalman Filter reduces both position error and velocity error compared with the original Extended Kalman Filter.