Magnetic field based heading estimation for pedestrian navigation environments

2011 ◽  
Author(s):  
Muhammad Haris Afzal ◽  
Valerie Renaudin ◽  
Gerard Lachapelle
Keyword(s):  
Magnetic Field ◽  
Pedestrian Navigation ◽  
Heading Estimation

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 Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 677 ◽  
Author(s):  
Zhi-An Deng ◽  
Guofeng Wang ◽  
Ying Hu ◽  
Yang Cui
Keyword(s):  
Pedestrian Navigation ◽  
Heading Estimation

scholarly journals Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelrahman Ali ◽  
Naser El-Sheimy
Keyword(s):  
Low Cost ◽  
Pressure Sensors ◽  
Micro Electro Mechanical System ◽  
Attitude Estimation ◽  
Handheld Devices ◽  
Accelerometer Data ◽  
Pedestrian Navigation ◽  
Heading Estimation ◽  
Sensors Fusion ◽  
New Research

The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments.

Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Average Distance ◽  
Low Cost ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Return ◽  
Measurement Units ◽  
Heading Estimation ◽  
Mems Imu

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

scholarly journals Heading Estimation for Indoor Pedestrian Navigation Using a Smartphone in the Pocket

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 21518-21536 ◽  
Author(s):  
Zhi-An Deng ◽  
Guofeng Wang ◽  
Ying Hu ◽  
Di Wu
Keyword(s):  
Pedestrian Navigation ◽  
Heading Estimation

scholarly journals An Accurate Heading Solution using MEMS-based Gyroscope and Magnetometer Integrated System (Preliminary Results)

2014 ◽  
Vol II-2 ◽  
pp. 75-78 ◽  
Author(s):  
M. El-Diasty
Keyword(s):  
Magnetic Field ◽  
Low Cost ◽  
Estimation Procedure ◽  
Integrated System ◽  
Static Mode ◽  
Earth Magnetic Field ◽  
Heading Estimation ◽  
High Level ◽  
Magnetic Disturbances ◽  
Over Time

An accurate heading solution is required for many applications and it can be achieved by high grade (high cost) gyroscopes (gyros) which may not be suitable for such applications. Micro-Electro Mechanical Systems-based (MEMS) is an emerging technology, which has the potential of providing heading solution using a low cost MEMS-based gyro. However, MEMS-gyro-based heading solution drifts significantly over time. The heading solution can also be estimated using MEMS-based magnetometer by measuring the horizontal components of the Earth magnetic field. The MEMS-magnetometer-based heading solution does not drift over time, but are contaminated by high level of noise and may be disturbed by the presence of magnetic field sources such as metal objects. This paper proposed an accurate heading estimation procedure based on the integration of MEMS-based gyro and magnetometer measurements that correct gyro and magnetometer measurements where gyro angular rates of changes are estimated using magnetometer measurements and then integrated with the measured gyro angular rates of changes with a robust filter to estimate the heading. The proposed integration solution is implemented using two data sets; one was conducted in static mode without magnetic disturbances and the second was conducted in kinematic mode with magnetic disturbances. The results showed that the proposed integrated heading solution provides accurate, smoothed and undisturbed solution when compared with magnetometerbased and gyro-based heading solutions.

scholarly journals Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration

2021 ◽  
pp. 002029402110218
Author(s):  
Xufei Cui ◽  
Yibing Li ◽  
Qiuying Wang ◽  
Malek Karaim ◽  
Aboelmagd Noureldin
Keyword(s):  
Magnetic Field ◽  
Inertial Sensor ◽  
Low Cost ◽  
Ferromagnetic Material ◽  
Estimation Algorithm ◽  
Integrated System ◽  
Local Magnetic Field ◽  
Navigation Systems ◽  
Heading Estimation ◽  
Iron Interference

The integrated INS/magnetometer measurement is widely used in low-cost navigation systems. The integration has proven more effective in suppressing the divergence of heading than relying solely on a magnetometer because this is susceptible to local magnetic field interference, reducing heading accuracy. Magnetometers sense the local magnetic field that may be interfered by the nearby ferromagnetic material or strong electric currents. Hence, the magnetometer must be calibrated in the vehicle before use. When a magnetometer is installed near power components (engines, etc.), soft iron interference can be ignored. In the vehicle’s external environment, the time-varying hard iron interference can reach 100 times the strength of the geomagnetic field, meaning that a magnetometer cannot function efficiently because its accuracy is so reduced. Hence, the constant hard magnetic interference inside the vehicle is mainly concerned in this paper. An INS/Magnetometer heading estimation algorithm based on a two-stage Kalman filter is proposed to solve the problem by combining inertial sensor and magnetometer with attitude information. In the first stage filter, the constant hard iron interference is estimated by setting upward standing the three IMU axes. In the second stage filter, the INS/Magnetometer heading estimation is implemented. Finally, the results show that the algorithm improves the accuracy of vehicle heading calculations.

scholarly journals Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhian Deng ◽  
Xin Liu ◽  
Zhiyu Qu ◽  
Changbo Hou ◽  
Weijian Si
Keyword(s):  
Inertial Sensors ◽  
Body Motion ◽  
Hand Movements ◽  
Estimation Errors ◽  
Daily Lives ◽  
Filtering Algorithm ◽  
Pedestrian Navigation ◽  
Heading Estimation ◽  
Estimation System ◽  
Previous User

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

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