Mobile robot's electronic compass calibration based on modified Fourier Neural Network

2011 ◽  
Author(s):  
Gong Kun ◽  
Deng Fang ◽  
Ma Tao
Keyword(s):  
Neural Network ◽  
Compass Calibration ◽  
Electronic Compass

Magnetic Sensors for Navigation Applications: An Overview

2013 ◽  
Vol 67 (2) ◽  
pp. 263-275 ◽  
Author(s):  
Wei Li ◽  
Jinling Wang
Keyword(s):  
Geomagnetic Field ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Current Trends ◽  
Compass Calibration ◽  
Global Navigation Satellite ◽  
Available Information ◽  
Electronic Compass

This paper reviews currently existing electronic magnetic sensor technologies for navigation applications. Magnetic compasses have been used in navigation for centuries. The Earth's geomagnetic field is considered to provide accurate, reliable and economically available information for orientation. Meanwhile, modern magnetometers and compass calibration technologies have allowed the electronic compass to become a crucial navigation tool, even in times of modern satellite navigation using Global Navigation Satellite Systems (GNSS). Magnetic sensor technologies, error modelling and compensating approaches have been reviewed in this paper. Current trends and the outlook for future development of the electronic compass are analysed.

Improving GNSS Navigation and Control with Electronic Compass in Unmanned System

2019 ◽  
Vol 23 (3) ◽  
pp. 427-436
Author(s):  
Xi Han ◽  
Xiaolin Zhang ◽  
Yuansheng Liu ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Satellite System ◽  
Back Propagation Neural Network ◽  
Angle Data ◽  
Compensation Technique ◽  
Unmanned System ◽  
Global Navigation Satellite ◽  
Electronic Compass

This paper proposes a compensation technique for the global navigation satellite system (GNSS)/real-time kinematic (RTK) course angle data using an electronic compass for an unmanned system. Additionally, the proportion, integral, and derivative control based on a back-propagation neural network (BP-PID) is introduced to improve the steering safety and riding comfort. The course angle jitter was determined. Because the GNSS/RTK receiver cannot offer stable heading data under specific conditions, including but not limited to susceptibility to obstacles, complex electromagnetic environment, and fewer satellites. The compensation algorithm is based on the determination of the GNSS course angle variance ratio and the asynchronous characteristic between the GNSS and an electronic compass. The combined data provide accurate and robust navigation information for an outdoor unmanned system. To address the limitation of the in-system parameter adjustment, a back-propagation (BP) neural network is adhibited to a conventional proportion, integral, and derivative (PID) lateral control system. The BP-PID control module updates the incremental PID parameters through self-learning, and results in the smoother operation of the vehicle. The flowchart of the learning algorithm and method of calculating the parameters are presented. A typical measurement was conducted and the obtained results were compared with typical RTK navigation results. Thus, the effectiveness of the proposed compensation method was confirmed.

Electronic Compass Calibration in Pedestrian Navigation System

2012 ◽  
Vol 490-495 ◽  
pp. 1246-1250
Author(s):  
Xiao Juan Zhang ◽  
Xi Sheng Li ◽  
Yi Bo Feng
Keyword(s):  
Magnetic Field ◽  
Navigation System ◽  
Direct Method ◽  
Dead Reckoning ◽  
Pedestrian Navigation ◽  
Movement Characteristic ◽  
Calibration Algorithm ◽  
Heading Angle ◽  
Compass Calibration ◽  
Electronic Compass

In this paper, a kind of pedestrian navigation system (PNS) that based on Earth’s magnetic field is introduced, and the error of the build-in electronic compass is analyzed, and an efficient calibration algorithm is presented. The PNS is determined pedestrian’s movement locus by calculating the heading angle and analyzing the movement characteristic, and then using the dead reckoning algorithm to combine the information together. The precision of PNS is affected by the error of the electric compass, because the heading angle is calculated from the magnetic field data measured by the compass. In order to reduce the measure error, a direct method which is used to calibrate the compass, based on ellipsoid fitting, is developed.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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