scholarly journals A New PDR Navigation Device for Challenging Urban Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Miguel Ortiz ◽  
Mathieu De Sousa ◽  
Valerie Renaudin
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Urban Environments ◽  
Privacy By Design ◽  
Handheld Device ◽  
Location Data ◽  
Pedestrian Navigation ◽  
New Research ◽  
Pedestrian Dead Reckoning ◽  
Original Device

The motivations, the design, and some applications of the new Pedestrian Dead Reckoning (PDR) navigation device, ULISS (Ubiquitous Localization with Inertial Sensors and Satellites), are presented in this paper. It is an original device conceived to follow the European recommendation of privacy by design to protect location data which opens new research toward self-contained pedestrian navigation approaches. Its application is presented with an enhanced PDR algorithm to estimate pedestrian’s footpaths in an autonomous manner irrespective of the handheld device carrying mode: texting or swinging. An analysis of real-time coding issues toward a demonstrator is also conducted. Indoor experiments, conducted with 3 persons, give a 5.8% mean positioning error over the 3 km travelled distances.

scholarly journals Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for Pedestrian Navigation

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 951 ◽  
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
Haonan Jia ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Accumulated Error ◽  
Pedestrian Dead Reckoning ◽  
Dominant Direction

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

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.

Research on WPS/PDR/MM Integrated Algorithm for Pedestrian Navigation and Positioning

2013 ◽  
Vol 437 ◽  
pp. 870-875 ◽  
Author(s):  
Zhong Liang Deng ◽  
Fei Peng Xie ◽  
Yan Pei Yu ◽  
Xiao Hong Zhao ◽  
Zhuang Yuan
Keyword(s):  
User Experience ◽  
Dead Reckoning ◽  
Positioning System ◽  
Map Matching ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Integrated Method ◽  
Pedestrian Navigation ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

In order to solve the discontinuity of navigation and positioning in indoor signal coverage blind areas, and false region judgment caused by positioning error, an integrated method combining Wireless Positioning System (WPS), Pedestrian Dead Reckoning (PDR) and Map Matching (MM) is presented in this paper. By using the combination of Kalman filtered WPS and PDR information, inertial information and geographic information, pedestrian position could be evaluated. Through experiment, this method effectively increased positioning accuracy of the system as well as greatly improved the user experience.

A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors

2019 ◽  
Vol 68 (8) ◽  
pp. 2996-3003 ◽  
Author(s):  
Ling-Feng Shi ◽  
Yu-Le Zhao ◽  
Gong-Xu Liu ◽  
Sen Chen ◽  
Yue Wang ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Pedestrian Dead Reckoning

scholarly journals Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ying Guo ◽  
Qinghua Liu ◽  
Xianlei Ji ◽  
Shengli Wang ◽  
Mingyang Feng ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Principal Component ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Using Data ◽  
The Time Domain ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

scholarly journals Maximum convergence algorithm for WiFi based indoor positioning system

2021 ◽  
Vol 11 (5) ◽  
pp. 4027
Author(s):  
Vinh Truong-Quang ◽  
Thong Ho-Sy
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Access Point ◽  
Indoor Positioning ◽  
Training Data ◽  
Computation Complexity ◽  
Positioning Systems ◽  
Nearest Neighbours ◽  
Indoor Positioning System ◽  
Pedestrian Dead Reckoning

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

scholarly journals Self-Contained Pedestrian Tracking With Mems Sensors

2021 ◽  
Author(s):  
Chengliang Huang
Keyword(s):  
Particle Filtering ◽  
Stance Phase ◽  
Field Experiments ◽  
Inertial Sensors ◽  
Tracking System ◽  
Dead Reckoning ◽  
Pedestrian Tracking ◽  
Indoor Wireless ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

Due to the limitations of current indoor wireless positioning technologies, a novel positioning/tracking solution has to be explored and developed, in order to locate a person anywhere anytime without any infrastructure. The purpose of this thesis is to present the result of the first phase of a long-period research to find such a solution and develop a practical system. In this thesis, using inertial sensors for positioning of people is selected to replace wireless solutions, considering the development of micro-electromechanical systems. A sensing module consisting of accelerometers, rate gyroscopes and magnetometers used to monitor human kinetics. In order to make this proposal practical, a synergy of existing strapdown inertial navigation and pedestrian dead-reckoning is proposed to improve the accuracy of positioning. Furthermore, the cyclic alternation of stance phase and swing phase in human walking is used to reduce errors accumulating during projection and integration of sensed accelerometer signals. Other than the improvement of some existing methods to detect stance phase and reset the velocity, several new methods are proposed to remove the integral drift during both phases of a human stride. The algorithm to calculate heading of on the sensing module is also deduced to limit the integral drift of rate gyroscopes. All the methods and algorithms are applied in field experiments with carefully chosen sensing module mounted on human footwear. The results show promising accuracy of tracking, hence validate the feasibility of self-contained pedestrian tracking system with inertial sensors. Further work, especially with map correlation and particle filtering, will be done in the coming phases of the project to make the system applicable both outdoor and indoor.

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