.png){kind=icon}
返回
Robust variational Bayesian method-based SINS/GPS integrated system
.png){kind=icon}
PRE
AI
DOI：10.1016/j.measurement.2022.110893.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
SINS/GPS integrated systems are influenced by non-Gaussian noise and unknown measurement noise due to exogenous disturbances and inaccurate noise statistics. To overcome this problem, a robust variational Bayesian method-based SINS/GPS integrated system is designed. First, the variational Bayesian-based Kalman filter is selected to estimate unknown measurement noise covariance. Second, the maximum correntropy criterion is introduced to the nonlinear robust filter to handle interference from non-Gaussian noise. Finally, the robust variational Bayesian method is designed based on the interacting multiple model, which not only fuses the variational Bayesian-based Kalman filter and the robust filter but also avoids non-Gaussian noise interference to the estimation result of measurement noise covariance. The robustness and adaptivity of the robust variational Bayesian method are verified by numerical simulation. Furthermore, the flight test results show improved performance of the SINS/GPS integrated system using the proposed method.
Keyword:
SINS
GPS integrated navigation
Sensors fusion
Interacting multiple model
Variational Bayesian
Robust Kalman filter
UAV
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
机构
引用论文
Infrared focal plane array attitude measurement method based on adaptive fault-tolerant extended Kalman filter.png)
MEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏分享收藏分享收藏Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter With Both Adaptivity and RobustnessIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages
PREAI
分享收藏Iterated maximum correntropy unscented Kalman filters for non-Gaussian systemsSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman FilteringIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
查看更多内容