Fault-Tolerant GPS/DR Integrated Navigation System Based on Heuristic Reduction of MEMS Inertial Measurement Unit Drift

2009 ◽  
Author(s):  
Qiang Zhang ◽  
Lei Wan ◽  
Yongjie Pang
Keyword(s):  
Navigation System ◽  
Inertial Measurement Unit ◽  
Fault Tolerant ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Inertial Measurement ◽  
Integrated Navigation System

A Design of MIMU/GPS Integrated Navigation System

2013 ◽  
Vol 278-280 ◽  
pp. 1719-1722 ◽  
Author(s):  
Xiao Yu Zhang ◽  
Chun Lei Song
Keyword(s):  
Navigation System ◽  
Inertial Measurement Unit ◽  
High Performance ◽  
Inertial Navigation System ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Test Results ◽  
Simulation Test ◽  
Integrated Navigation System ◽  
Navigation Algorithm

A new scheme of small integrated navigation system based on micro inertial measurement unit (MIMU), global position system (GPS) is presented. The characteristic of these sensors and the structure of system are introduced respectively. The TI high performance floating point DSP TMS320C6713B is used as core processor, which is designed to realize both the data collecting and the navigation calculating. According to the error models of inertial navigation system, an integrated navigation algorithm used Kalman filter is proposed to fuse the information from all of the sensors. The simulation test results show the feasibility of the system design.

scholarly journals Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1527
Author(s):  
Jiangtao Zheng ◽  
Sihai Li ◽  
Shiming Liu ◽  
Bofan Guan ◽  
Dong Wei ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Estimation Model ◽  
Positioning Accuracy ◽  
Inertial Measurement ◽  
Integrated Navigation System ◽  
Positioning Method

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

scholarly journals A SINS/BDS Integrated Navigation Method Based on Classified Weighted Adaptive Filtering

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Xuchao Kang ◽  
Guangjun He ◽  
Xingge Li
Keyword(s):  
Adaptive Filtering ◽  
Navigation System ◽  
Fault Tolerant ◽  
Hypothesis Test ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Covariance Matching ◽  
Uncertain Model ◽  
Navigation Accuracy ◽  
Navigation Method

Aiming at the problem that the accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed. Firstly, the innovation covariance matching technology is used to detect whether there is any abnormality in the system as a whole. Then the types of anomalies are distinguished by hypothesis test. Different types of anomalies have different effects on state estimation. Based on the dynamic changes of innovation, different adaptive weighting methods are adopted to correct navigation information. The simulation results show that this method can effectively improve the fault-tolerant performance of integrated navigation system in complex environment with unknown anomaly types. When both model anomalies and observation anomalies exist, the speed and position accuracy are increased by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering, which has higher navigation accuracy.

scholarly journals A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosu Xu ◽  
Peijuan Li ◽  
Jian-juan Liu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Fault Tolerant ◽  
Doppler Velocity ◽  
Observation Data ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Data Set ◽  
Integrated Navigation System ◽  
Short Period

The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.

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