scholarly journals Indoor Positioning Based on Pedestrian Dead Reckoning and Magnetic Field Matching for Smartphones

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4142 ◽  
Author(s):  
Jian Kuang ◽  
Xiaoji Niu ◽  
Peng Zhang ◽  
Xingeng Chen
Keyword(s):  
Magnetic Field ◽  
Estimation Error ◽  
Magnetic Field Gradient ◽  
Single Point ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Shopping Mall ◽  
Mm Algorithm ◽  
Field Matching ◽  
Pedestrian Dead Reckoning

This paper presents an ambient magnetic field map-based matching (MM) positioning algorithm for smartphones in an indoor environment. To improve the low distinguishability of a magnetic field fingerprint at a single point, a magnetic field sequence (MFS) combined with the measured trajectory contour coming from pedestrian dead-reckoning (PDR) is used for MM. Based on the fast approximation of magnetic field gradient, a Gauss-Newton iterative (GNI) method is used to find a rigid transformation that optimally aligns the measured MFS with a reference MFS coming from the magnetic field map. Then, the position of the reference MFS is used to control the position drift error of the inertial navigation system (INS) based PDR by an extended Kalman filter (EKF) and to further improve the accuracy of the trajectory contour. Finally, we conduct several experiments to evaluate the navigation performance of the proposed MM algorithm. The test results show that the position estimation error of the MM algorithm is 0.64 m (RMS) in an office building environment, 1.87 m (RMS) in a typical lobby environment, and 2.34 m (RMS) in a shopping mall environment.

scholarly journals Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5343
Author(s):  
Miroslav Opiela ◽  
František Galčík
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Shopping Mall ◽  
Bayesian Filtering ◽  
Current Position ◽  
Positioning Systems ◽  
Time Calculation ◽  
Pedestrian Dead Reckoning ◽  
Grid Based

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper.

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.

Single point measurements of magnetic field gradient waveform

2003 ◽  
Vol 163 (1) ◽  
pp. 1-7 ◽  
Author(s):  
David J. Goodyear ◽  
Maurice Shea ◽  
Steven D. Beyea ◽  
Nadim J. Shah ◽  
Bruce J. Balcom
Keyword(s):  
Magnetic Field ◽  
Field Gradient ◽  
Magnetic Field Gradient ◽  
Single Point ◽  
Gradient Waveform

scholarly journals A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

2019 ◽  
Vol 9 (18) ◽  
pp. 3727
Author(s):  
Chai ◽  
Chen ◽  
Wang
Keyword(s):  
Kalman Filter ◽  
Autoregressive Model ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Average Error ◽  
Adaptive Kalman Filter ◽  
Position Information ◽  
Variance Matrix ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.

scholarly journals LLIO: Lightweight Learned Inertial Odometer

2021 ◽  
Author(s):  
Yan Wang ◽  
Jian Kuang ◽  
xiaoji niu
Keyword(s):  
Mobile Devices ◽  
Ground Plane ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Data Driven ◽  
Trajectory Estimation ◽  
Motion Patterns ◽  
Similar Accuracy ◽  
Low Drift ◽  
Pedestrian Dead Reckoning

<div><div>The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.</div><div>The traditional gait model-based methods cannot fulfill the various motion patterns.</div><div>And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.</div><div>TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.</div><div>However, TLIO is unsuitable for mobile devices because it is computationally expensive.</div><div>In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.</div><div>By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.</div><div>Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.</div><div>The inference efficiency of the proposed system is 2-12 times that of TLIO.</div></div>

scholarly journals Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 185
Author(s):  
Fang-Shii Ning ◽  
Yu-Chun Chen
Keyword(s):  
Magnetic Field ◽  
Particle Filter ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Filter Method ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Pedestrian Dead Reckoning

Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.

scholarly journals Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5726
Author(s):  
Makia Zmitri ◽  
Hassen Fourati ◽  
Christophe Prieur
Keyword(s):  
Magnetic Field ◽  
Field Gradient ◽  
Inertial Sensors ◽  
Magnetic Field Gradient ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Velocity Estimation ◽  
Gradient Based ◽  
Estimation Scheme ◽  
The Magnetic Field

This paper proposes an advanced solution to improve the inertial velocity estimation of a rigid body, for indoor navigation, through implementing a magnetic field gradient-based Extended Kalman Filter (EKF). The proposed estimation scheme considers a set of data from a triad of inertial sensors (accelerometer and gyroscope), as well as a determined arrangement of magnetometers array. The inputs for the estimation scheme are the spatial derivatives of the magnetic field, from the magnetometers array, and the attitude, from the inertial sensors. As shown in the literature, there is a strong relation between the velocity and the measured magnetic field gradient. However, the latter usually suffers from high noises. Then, the novelty of the proposed EKF is to develop a specific equation to describe the dynamics of the magnetic field gradient. This contribution helps to filter, first, the magnetic field and its gradient and second, to better estimate the inertial velocity. Some numerical simulations that are based on an open source database show the targeted improvements. At the end of the paper, this approach is extended to position estimation in the case of a foot-mounted application and the results are very promising.

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