scholarly journals Pedestrian Dead Reckoning based Position Estimation Scheme considering Pedestrian's Various Movement Type under Combat Environments

2016 ◽  
Vol 17 (10) ◽  
pp. 609-617
Author(s):  
SangHoon Park ◽  
Jongmok Chae ◽  
Jang-Myung Lee
Keyword(s):  
Dead Reckoning ◽  
Position Estimation ◽  
Estimation Scheme ◽  
Movement Type ◽  
Pedestrian Dead Reckoning

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 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 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 S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 148-176
Author(s):  
Maan Khedr ◽  
Naser El-Sheimy
Keyword(s):  
Smart Cities ◽  
Dead Reckoning ◽  
Human Motion ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Short Term ◽  
Navigation Solution ◽  
Correction Technique ◽  
Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

scholarly journals Sensor Modeling for Underwater Localization Using a Particle Filter

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1549
Author(s):  
Humberto Martínez-Barberá ◽  
Pablo Bernal-Polo ◽  
David Herrero-Pérez
Keyword(s):  
Sequential Monte Carlo ◽  
Sensory Information ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Position Estimation ◽  
Sources Of Uncertainty ◽  
Sensor Signals ◽  
Underwater Navigation ◽  
Underwater Localization ◽  
Sensor Modeling

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

scholarly journals Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 397
Author(s):  
Hossein Shoushtari ◽  
Thomas Willemsen ◽  
Harald Sternberg
Keyword(s):  
Mobile Networks ◽  
Dead Reckoning ◽  
Simulated Data ◽  
Minimum Cost ◽  
Cost Effective ◽  
Satellite System ◽  
Position Estimation ◽  
Robust Solution ◽  
Position Data ◽  
Global Navigation Satellite

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

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