Performance Improvement of Azimuth Estimation in Low Cost MEMS IMU based INS/GPS Integrated Navigation System

2012 ◽  
Vol 16 (5) ◽  
pp. 738-743 ◽  
Author(s):  
Se-Bum Chun ◽  
Moon-Beom Heo
Keyword(s):  
Performance Improvement ◽  
Navigation System ◽  
Low Cost ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Mems Imu

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.

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.

scholarly journals Low-Cost Navigation and Guidance Systems for Unmanned Aerial Vehicles — Part 1: Vision-Based and Integrated Sensors

2012 ◽  
Vol 19 (2) ◽  
pp. 71-98 ◽  
Author(s):  
Roberto Sabatini ◽  
Celia Bartel ◽  
Anish Kaharkar ◽  
Tesheen Shaid ◽  
Leopoldo Rodriguez ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
System Architecture ◽  
Navigation System ◽  
Low Cost ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Aerial Vehicles ◽  
Vision Based Navigation ◽  
Mems Imu

Abstract In this paper we present a new low-cost navigation system designed for small size Unmanned Aerial Vehicles (UAVs) based on Vision-Based Navigation (VBN) and other avionics sensors. The main objective of our research was to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of a small UAV, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN were compared and the Appearance-Based Approach (ABA) was selected for implementation. Feature extraction and optical flow techniques were employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we addressed the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, as well as the aiding from Aircraft Dynamics Models (ADMs). In particular, by employing these sensors/models, we aimed to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. An Extended Kalman Filter (EKF) was developed to fuse the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Two different integrated navigation system architectures were implemented. The first used VBN at 20 Hz and GPS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also included the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. Simulation of these two modes was accomplished in a significant portion of the AEROSONDE UAV operational flight envelope and performing a variety of representative manoeuvres (i.e., straight climb, level turning, turning descent and climb, straight descent, etc.). Simulation of the first integrated navigation system architecture (VBN/IMU/GPS) showed that the integrated system can reach position, velocity and attitude accuracies compatible with CAT-II precision approach requirements. Simulation of the second system architecture (VBN/IMU/GPS/ADM) also showed promising results since the achieved attitude accuracy was higher using the ADM/VBS/IMU than using VBS/IMU only. However, due to rapid divergence of the ADM virtual sensor, there was a need for frequent re-initialisation of the ADM data module, which was strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed

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 Application of Low Cost Integrated Navigation System in Precision Agriculture

2020 ◽  
Vol 26 (4) ◽  
pp. 1433-1442
Author(s):  
Qi Wang ◽  
Changsong Yang ◽  
Yuxiang Wang ◽  
Shao-en Wu
Keyword(s):  
Precision Agriculture ◽  
Navigation System ◽  
Low Cost ◽  
Integrated Navigation ◽  
Integrated Navigation System
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