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.

scholarly journals Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4782 ◽  
Author(s):  
Yong Hun Kim ◽  
Min Jun Choi ◽  
Eung Ju Kim ◽  
Jin Woo Song
Keyword(s):  
Magnetic Field ◽  
Importance Sampling ◽  
Dead Reckoning ◽  
Position Error ◽  
Magnetic Sensors ◽  
Map Matching ◽  
Multiple Sensors ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Pedestrian Dead Reckoning

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

scholarly journals A Real-Time GNSS/PDR Navigation System for Mobile Devices

2021 ◽  
Vol 13 (8) ◽  
pp. 1567
Author(s):  
Michele Basso ◽  
Alessio Martinelli ◽  
Simone Morosi ◽  
Fabrizio Sera
Keyword(s):  
Energy Saving ◽  
Navigation System ◽  
Dead Reckoning ◽  
Step Length ◽  
The Body ◽  
Global Navigation Satellite Systems ◽  
Motion Direction ◽  
Trade Off ◽  
Pedestrian Navigation ◽  
Position Accuracy

In this article, a smart pedestrian navigation system is developed to be implemented in a common smartphone. The main phases that characterize a pedestrian navigation system that is based on dead reckoning are introduced. A suitable Phase-Locked Loop is designed and the algorithm to estimate the direction of the user’s motion between one step and the next is developed. Finally, a suitable multi-rate Kalman filter (KF) is considered to merge the information from the pedestrian dead reckoning (PDR) navigation with the data provided by the global navigation satellite systems (GNSS). The proposed GNSS/PDR navigation system is implemented in Simulink as a finite-state machine and allows to define a trade-off between energy-saving and performance improvement in terms of position accuracy. The presented pedestrian navigation system is independent of the body-worn location of the smartphone and implements a compensation strategy of the systematic errors that are committed on the step-length estimation and the determination of the motion direction. Moreover, several tests are performed by walking in urban and suburban environments: the results show that a suitable trade-off between energy-saving and position accuracy can be reached by switching the GNSS receiver on and off.

The Standing Calibration Method of MEMS Gyro Bias for Autonomous Pedestrian Navigation System

2016 ◽  
Vol 70 (3) ◽  
pp. 607-617 ◽  
Author(s):  
Yanshun Zhang ◽  
Xu Yang ◽  
Xiangming Xing ◽  
Zhanqing Wang ◽  
Yunqiang Xiong
Keyword(s):  
Navigation System ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Calibration Method ◽  
Micro Electro Mechanical System ◽  
Human Motion ◽  
Pedestrian Navigation ◽  
Initial Stage ◽  
Angular Velocities ◽  
Gyro Bias

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.

scholarly journals Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Ji Hyoung Ryu ◽  
Ganduulga Gankhuyag ◽  
Kil To Chong
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Navigation Systems ◽  
Position Information ◽  
Global Positioning ◽  
Heading Angle ◽  
Electronic Compass

Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm.

On In Situ Calibration of SINS and Doppler Dead Reckoning Navigation System

2012 ◽  
Vol 479-481 ◽  
pp. 2610-2615
Author(s):  
Kai Yao ◽  
Qi Dan Zhu ◽  
Bo Zhang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Strapdown Inertial Navigation System ◽  
Doppler Velocity ◽  
Navigation Algorithm ◽  
Calibration Algorithm ◽  
Strapdown Inertial Navigation

This paper addresses a practical problem arising in the calibration of bottom-lock doppler velocity log for the navigation of surface ships. Firstly, a dead reckoning navigation algorithm and briefly error analyze are proposed. Then, employing ship’s true trajectory and calculated trajectory, the rotational alignment offset between a bottom-lock doppler velocity log and a strapdown inertial navigation system as well as the scale factor error of the doppler velocity log can be experimentally determined using sensors commonly deployed with a vehicle in the field. It requires velocity values from the vehicle's doppler log and strapdown inertial navigation system, and absolute vehicle position fixes from a GPS receiver. Lake experiment results show that the calibration algorithm can calibrate the error parameters effectively, thus the position error decreases significantly after compensating the error parameters.

A method for dead reckoning parameter correction in pedestrian navigation system

2003 ◽  
Vol 52 (1) ◽  
pp. 209-215 ◽  
Author(s):  
R. Jirawimut ◽  
P. Ptasinski ◽  
V. Garaj ◽  
F. Cecelja ◽  
W. Balachandran
Keyword(s):  
Navigation System ◽  
Dead Reckoning ◽  
Pedestrian Navigation ◽  
Parameter Correction

scholarly journals RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1791
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Hongji Cao ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
...  
Keyword(s):  
Real Time ◽  
High Precision ◽  
Navigation System ◽  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Pedestrian Navigation

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

scholarly journals Drift Reduction in Pedestrian Navigation System by Exploiting Motion Constraints and Magnetic Field

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1455 ◽  
Author(s):  
Muhammad Ilyas ◽  
Kuk Cho ◽  
Seung-Ho Baeg ◽  
Sangdeok Park
Keyword(s):  
Magnetic Field ◽  
Navigation System ◽  
Motion Constraints ◽  
Pedestrian Navigation
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