scholarly journals On-Line Smoothing for an Integrated Navigation System with Low-Cost MEMS Inertial Sensors

Sensors ◽  
2012 ◽  
Vol 12 (12) ◽  
pp. 17372-17389 ◽  
Author(s):  
Kai-Wei Chiang ◽  
Thanh Duong ◽  
Jhen-Kai Liao ◽  
Ying-Chih Lai ◽  
Chin-Chia Chang ◽  
...  
Keyword(s):  
Navigation System ◽  
Inertial Sensors ◽  
Low Cost ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
On Line

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.

Design of a low-cost attitude determination GPS/INS integrated navigation system

GPS Solutions ◽  
2005 ◽  
Vol 9 (4) ◽  
pp. 294-311 ◽  
Author(s):  
Dong-Hwan Hwang ◽  
Sang Heon Oh ◽  
Sang Jeong Lee ◽  
Chansik Park ◽  
Chris Rizos
Keyword(s):  
Navigation System ◽  
Attitude Determination ◽  
Low Cost ◽  
Integrated Navigation ◽  
Integrated Navigation System

scholarly journals Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chong Shen ◽  
Zesen Bai ◽  
Huiliang Cao ◽  
Ke Xu ◽  
Chenguang Wang ◽  
...  
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
Low Cost ◽  
Integrated Navigation ◽  
Static State ◽  
Integrated Navigation System ◽  
Microaerial Vehicles ◽  
Flow Information ◽  
Optical Flow Sensor ◽  
Navigation Scheme

The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

scholarly journals Estimation of UAV Position with Use of Smoothing Algorithms

2017 ◽  
Vol 24 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Piotr Kaniewski ◽  
Rafał Gil ◽  
Stanisław Konatowski
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Position Estimation ◽  
Fixed Interval ◽  
Smoothing Algorithm ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Gnss Receiver ◽  
On Line

Abstract The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

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