Integrity assurance of Kalman-filter based GNSS/IMU integrated systems against IMU faults for UAV applications

2018 ◽  
Author(s):  
Jinsil Lee ◽  
Minchan Kim ◽  
Jiyun Lee ◽  
Sam Pullen
Keyword(s):  
Kalman Filter ◽  
Integrated Systems ◽  
Integrity Assurance

On Designing PMI Kalman Filter for INS/GPS Integrated Systems With Unknown Sensor Errors

2015 ◽  
Vol 15 (1) ◽  
pp. 535-544 ◽  
Author(s):  
Maiying Zhong ◽  
Jia Guo ◽  
Quan Cao
Keyword(s):  
Kalman Filter ◽  
Integrated Systems ◽  
Sensor Errors

scholarly journals Randomly Weighted CKF for Multisensor Integrated Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Hua Zong ◽  
Zhaohui Gao ◽  
Wenhui Wei ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Method ◽  
Experimental Results ◽  
Integrated Systems ◽  
System State ◽  
System Error ◽  
Random Weights ◽  
Cubature Kalman Filter ◽  
Dynamic Modification

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

scholarly journals An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3091 ◽  
Author(s):  
Yipeng Ning ◽  
Jian Wang ◽  
Houzeng Han ◽  
Xinglong Tan ◽  
Tianjun Liu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Basis Function ◽  
Navigation System ◽  
Urban Areas ◽  
Rbf Neural Network ◽  
Integrated Systems ◽  
Model Errors ◽  
Robust Kalman Filter

Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network’s output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

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