scholarly journals Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3149 ◽  
Author(s):  
Jiheon Kang ◽  
Joonbeom Lee ◽  
Doo-Seop Eom
Keyword(s):  
Indoor Localization ◽  
Inertial Sensor ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Android Application ◽  
Sensory Signal ◽  
Novel Method ◽  
The Cost ◽  
Deep Learning Model ◽  
Pedestrian Dead Reckoning

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

scholarly journals A Loose-Coupled Fusion of Inertial and UWB Assisted by a Decision-Making Algorithm for Localization of Emergency Responders

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1463 ◽  
Author(s):  
André G. Ferreira ◽  
Duarte Fernandes ◽  
André P. Catarino ◽  
Ana M. Rocha ◽  
João L. Monteiro
Keyword(s):  
Decision Making ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Time Of Arrival ◽  
Robust Solution ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Emergency Responders ◽  
Pedestrian Dead Reckoning ◽  
Non Line Of Sight

Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs). These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision-making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non-line-of-sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time-of-arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.

scholarly journals iPAC: Integrate Pedestrian Dead Reckoning and Computer Vision for Indoor Localization and Tracking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 183514-183523 ◽  
Author(s):  
Danyang Li ◽  
Yumeng Lu ◽  
Jingao Xu ◽  
Qiang Ma ◽  
Zhuo Liu
Keyword(s):  
Computer Vision ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Localization And Tracking ◽  
Pedestrian Dead Reckoning

PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization

2014 ◽  
Vol 701-702 ◽  
pp. 989-993
Author(s):  
Wen Bin Yu ◽  
Peng Li ◽  
Zhi Chen ◽  
Chang Li
Keyword(s):  
Social Network ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Fingerprint Matching ◽  
Network Applications ◽  
Wireless Signal ◽  
Signal Fluctuation ◽  
Pedestrian Dead Reckoning ◽  
Wifi Fingerprint

Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor localization methods.

scholarly journals INDOOR LOCALIZATION USING ACCELEROMETER AND GYROSCOPE SMARTPHONE BASED

2021 ◽  
Vol 2 (2) ◽  
pp. 119-126
Author(s):  
Anggreini Intan Permata Sari ◽  
Arkham Zahri Rakhman
Keyword(s):  
Error Rate ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Initial Position ◽  
Sensor System ◽  
System Testing ◽  
Accelerometer Sensor ◽  
Campus Environment ◽  
Gyroscope Sensor ◽  
Pedestrian Dead Reckoning

Indoor localization is one of the more accurate technologies to be used to determine indoors or buildings. Pedestrian Dead Reckoning (PDR) is a method of determining the user's position by adding a method that occurs to a known initial position. The displacement that occurs is estimated with the help of an accelerometer sensor attached to the user as a step detector and to determine the direction towards the user using a gyroscope sensor. System testing is carried out in the Institut Teknologi Sumatera’s campus environment on the 2nd floor of Building C and D. The results from the detection of steps get an error rate of 1.13% using a threshold of 0.8.

scholarly journals Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8180
Author(s):  
Jijun Geng ◽  
Linyuan Xia ◽  
Jingchao Xia ◽  
Qianxia Li ◽  
Hongyu Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Actual Measurement ◽  
Location Service ◽  
Cubature Kalman Filter ◽  
Robust Adaptive ◽  
Pedestrian Dead Reckoning

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

scholarly journals Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Rashid Ali ◽  
Ran Liu ◽  
Anand Nayyar ◽  
Basit Qureshi ◽  
Zhiqiang Cao
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Pedestrian Dead Reckoning

scholarly journals INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES

2018 ◽  
Vol XLII-4 ◽  
pp. 253-260
Author(s):  
E. Gulo ◽  
G. Sohn ◽  
A. Afnan
Keyword(s):  
Mobile Devices ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Viable Approach ◽  
Similar Accuracy ◽  
Pedestrian Dead Reckoning

<p><strong>Abstract.</strong> With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.</p>

scholarly journals Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 763 ◽  
Author(s):  
Ryosuke Ichikari ◽  
Katsuhiko Kaji ◽  
Ryo Shimomura ◽  
Masakatsu Kourogi ◽  
Takashi Okuma ◽  
...  
Keyword(s):  
Test Data ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Measured Data ◽  
Practical Performance ◽  
Testing Environments ◽  
The Many ◽  
Warehouse Operations ◽  
Pedestrian Dead Reckoning ◽  
Unique Capability

The performance of indoor localization methods is highly dependent on the situations in which they are used. Various competitions on indoor localization have been held for fairly comparing the existing indoor localization methods in shared and controlled testing environments. However, it is difficult to evaluate the practical performance in industrial scenarios through the existing competitions. This paper introduces two indoor localization competitions, which are named the “PDR Challenge in Warehouse Picking 2017” and “xDR Challenge for Warehouse Operations 2018” for tracking workers and vehicles in a warehouse scenario. For the PDR Challenge in Warehouse Picking 2017, we conducted a unique competition based on the data measured during the actual picking operation in an actual warehouse. We term the dead-reckoning of a vehicle as vehicle dead-reckoning (VDR), and the term “xDR” is derived from pedestrian dead-reckoning (PDR) plus VDR. As a sequel competition of the PDR Challenge in Warehouse Picking 2017, the xDR Challenge for Warehouse Operations 2018 was conducted as the world’s first competition that deals with tracking forklifts by VDR with smartphones. In the paper, first, we briefly summarize the existing competitions, and clarify the characteristics of our competitions by comparing them with other competitions. Our competitions have the unique capability of evaluating the practical performance in a warehouse by using the actual measured data as the test data and applying multi-faceted evaluation metrics. As a result, we successfully organize the competitions due to the many participants from many countries. As a conclusion of the paper, we summarize the findings of the competitions.

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