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 Drift Control of Pedestrian Dead Reckoning (PDR) for Long Period Navigation under Different Smartphone Poses

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Ahmed Mansour ◽  
Wu Chen ◽  
Huan Luo ◽  
Yaxin Li ◽  
Jingxian Wang ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Field Measurements ◽  
Step Length ◽  
Detection Algorithm ◽  
Long Period ◽  
Walking Pattern ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

The inherent errors of low-cost inertial sensors cause significant heading drift that accumulates over time, making it difficult to rely on Pedestrian Dead Reckoning (PDR) for navigation over a long period. Moreover, the flexible portability of the smartphone poses a challenge to PDR, especially for heading determination. In this work, we aimed to control the PDR drift under the conditions of the unconstrained smartphone to eventually enhance the PDR performance. To this end, we developed a robust step detection algorithm that efficiently captures the peak and valley events of the triggered steps regardless of the device’s pose. The correlation between these events was then leveraged as distinct features to improve smartphone pose detection. The proposed PDR system was then designed to select the step length and heading estimation approach based on a real-time walking pattern and pose discrimination algorithm. We also leveraged quasi-static magnetic field measurements that have less disturbance for estimating reliable compass heading and calibrating the gyro heading. Additionally, we also calibrated the step length and heading when a straight walking pattern is observed between two base nodes. Our results showed improved device pose recognition accuracy. Furthermore, robust and accurate results were achieved for step length, heading and position during long-term navigation under unconstrained smartphone conditions.

scholarly journals A Pedestrian Dead Reckoning System Integrating Low - Cost MEMS Inertial Sensors and GPS Receiver

2014 ◽  
Vol 7 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Jin - feng Li ◽  
◽  
Qing - hui Wang ◽  
Xiao - mei Liu ◽  
Shun Cao ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Gps Receiver ◽  
Pedestrian Dead Reckoning

An Intelligent Navigation Solution for Land Mobile Location-Based Services

2002 ◽  
Vol 55 (2) ◽  
pp. 225-240 ◽  
Author(s):  
Stephen Scott-Young ◽  
Allison Kealy
Keyword(s):  
Firm Growth ◽  
Spatial Information ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Geographical Information ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Lower Accuracy

The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.

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 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.

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