A novel fusion method for DR/GPS integrated navigation system

2008 ◽  
Author(s):  
Santong Zhang ◽  
Shiwu Yang
Keyword(s):  
Navigation System ◽  
Fusion Method ◽  
Integrated Navigation ◽  
Integrated Navigation System

A Ship GPS/DR Integrated Navigation System

2012 ◽  
Vol 591-593 ◽  
pp. 1818-1821
Author(s):  
Yuan Liang Zhang
Keyword(s):  
Data Fusion ◽  
Navigation System ◽  
Global Economy ◽  
Dead Reckoning ◽  
Fusion Method ◽  
Integrated Navigation ◽  
Gps Receiver ◽  
Integrated Navigation System ◽  
Ship Navigation ◽  
Navigation Information

The development of the global economy stimulates the exploitation of the ocean. A precise and stable ship navigation system is very important for people to explore the ocean. Dead reckoning (DR) system is a frequently used navigation system for sailing in the ocean. It can provide precise short term navigation information but the error of DR system can accumulate over time without limitation. GPS can be used for localization and navigation in outside environment. Although the SA policy was removed the accuracy of GPS for civilian use is still big. But the errors of GPS are bounded. Since the complementarity of DR and GPS system the integrated GPS/DR system can provide good navigation results. In this paper a new Kalman filter based DR/GPS data fusion method is proposed. This method is designed based on the characteristic of the GPS receiver. By using this data fusion method the cheap GPS receiver can cooperate with DR system to provide precise navigation information for ships. Simulation is conducted to validate the proposed fusion method. The good result shows the potential of this fusion method for the ship navigation.

scholarly journals Information Fusion Based on Complementary Filter for SINS/CNS/GPS Integrated Navigation System of Aerospace Plane

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7193
Author(s):  
Yanming Zhao ◽  
Gongmin Yan ◽  
Yongyuan Qin ◽  
Qiangwen Fu
Keyword(s):  
Real Time ◽  
Information Fusion ◽  
Navigation System ◽  
Physical Simulation ◽  
Fusion Method ◽  
Integrated Navigation ◽  
Real Time System ◽  
Integrated Navigation System ◽  
Complementary Filter ◽  
Aerospace Plane

In order to solve the problems of heavy computational load and poor real time of the information fusion method based on the federated Kalman filter (FKF), a novel information fusion method based on the complementary filter is proposed for strapdown inertial navigation (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system of an aerospace plane. The complementary filters are designed to achieve the estimations of attitude, velocity, and position in the SINS/CNS/GPS integrated navigation system, respectively. The simulation results show that the proposed information fusion method can effectively realize SINS/CNS/GPS information fusion. Compared with FKF, the method based on complementary filter (CF) has the advantages of simplicity, small calculation, good real-time performance, good stability, no need for initial alignment, fast convergence, etc. Furthermore, the computational efficiency of CF is increased by 94.81%. Finally, the superiority of the proposed CF-based method is verified by both the semi-physical simulation and real-time system experiment.

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.

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