scholarly journals Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4554 ◽  
Author(s):  
Hongyu Zhao ◽  
Wanli Cheng ◽  
Ning Yang ◽  
Sen Qiu ◽  
Zhelong Wang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Vertical Displacement ◽  
Location Based Services ◽  
Map Matching ◽  
Matching Algorithm ◽  
Positioning Systems ◽  
Indoor Location ◽  
Wall Crossing

Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).

scholarly journals INDOOR POSITIONING FOR SMART DEVICES BASED ON SENSOR FUSION WITH PARTICLE FILTER: LOCALIZATION AND MAP UPDATING

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 259-266
Author(s):  
Y. Yang ◽  
C. Toth
Keyword(s):  
Particle Filter ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Visual Localization ◽  
Positioning Systems ◽  
Indoor Location ◽  
Map Updating ◽  
Smart Mobile ◽  
New Sensors

Abstract. With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

2021 ◽  
pp. 1-16
Author(s):  
Xiaohan Wang ◽  
Pei Wang ◽  
Weilong Chen ◽  
Wangwu Hu ◽  
Long Yang
Keyword(s):  
Road Network ◽  
Transition Probability ◽  
Location Based Services ◽  
Original Position ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Low Efficiency

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

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.

Navigation Based on Sensors in Smartphones

2018 ◽  
pp. 368-396
Author(s):  
Guenther Retscher ◽  
Allison Kealy
Keyword(s):  
Navigation System ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Activity Monitoring ◽  
Navigation Systems ◽  
Location Tracking ◽  
Map Matching ◽  
Complex Environments ◽  
Positioning Systems ◽  
Cooperative Positioning

With the increasing ubiquity of smartphones and tablets, users are now routinely carrying a variety of sensors with them wherever they go. These devices are enabling technologies for ubiquitous computing, facilitating continuous updates of a user's context. They have built-in MEMS-based accelerometers for ubiquitous activity monitoring and there is a growing interest in how to use these together with gyroscopes and magnetometers to build dead reckoning (DR) systems for location tracking. Navigation in complex environments is needed mainly by consumer users, private vehicles, and pedestrians. Therefore, the navigation system has to be small, easy to use, and have reasonably low levels of power consumption and price. The technologies and techniques discussed here include the fusion of inertial navigation (IN) and other sensors, positioning based on signals from wireless networks (such as Wi-Fi), image-based methods, cooperative positioning systems, and map matching (MM). The state-of-the-art of MEMS-based location sensors and their integration into modern navigation systems are also presented.

scholarly journals LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1950
Author(s):  
David Gualda ◽  
María Carmen Pérez-Rubio ◽  
Jesús Ureña ◽  
Sergio Pérez-Bachiller ◽  
José Manuel Villadangos ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Acoustic Waves ◽  
Graph Matching ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Positioning Systems

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.

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.

Localization Experiments Based on Inertial Sensors for Navigation Applications

2015 ◽  
Vol 764-765 ◽  
pp. 550-554
Author(s):  
Yih Shyh Chiou ◽  
Fuan Tsai
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Mobile Terminal ◽  
Difficult Problem ◽  
Location Estimation ◽  
Base Stations ◽  
Measurement Unit ◽  
Positioning Systems ◽  
Short Period ◽  
Radio Signals

Robust and accurate positioning systems with seamless outdoor and indoor coverage have been receiving a great deal of attention. In outdoor environments, as radio signals transmitted from base stations or satellites are jammed or shielded, to estimate an accurate location using only the absolute positioning scheme remains a difficult problem regarding location accuracy. In order to overcome the drawback of the ranging scheme based on the radio signals, this paper presents a positioning approach based on inertial-measurement-unit (IMU) observations to estimate the location of a mobile terminal (MT). For the location-estimation technique, the positioning experiment is handled by the dead-reckoning (DR) algorithm. By processing the observations from the IMU, it is possible to estimate the movement of an MT (car). In terms of the IMU-based approach, although the experimental results demonstrate that the positioning scheme using the DR algorithm causes the error propagation, the approach can work in short period of time for navigation applications.

scholarly journals Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2283
Author(s):  
Peter Brida ◽  
Juraj Machaj ◽  
Jan Racko ◽  
Ondrej Krejcar
Keyword(s):  
Dead Reckoning ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Reference Points ◽  
Location Based Services ◽  
Position Information ◽  
Vast Number ◽  
Positioning Systems ◽  
Time Required ◽  
Radio Map

While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

scholarly journals Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 375 ◽  
Author(s):  
Ghulam Hussain ◽  
Muhammad Jabbar ◽  
Jun-Dong Cho ◽  
Sangmin Bae
Keyword(s):  
Short Term Memory ◽  
Inertial Sensors ◽  
Recognition Task ◽  
Indoor Positioning ◽  
Physical Activities ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Average Error ◽  
Positioning Systems ◽  
Localization Performance

The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m2. Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities.

Research of Error Compensation Based on DR/GIS Integrated Navigation

2012 ◽  
Vol 229-231 ◽  
pp. 1793-1797
Author(s):  
Jian Guo Xu ◽  
Zhi Li Zhang ◽  
Zhao Fa Zhou ◽  
Zhong Xin Li
Keyword(s):  
Autonomous Navigation ◽  
Dead Reckoning ◽  
Map Matching ◽  
Integrated Navigation ◽  
Initial Alignment ◽  
Matching Algorithm ◽  
Navigation Errors ◽  
Gyro Drift ◽  
Dynamic Alignment ◽  
Navigation Method

Dead Reckoning is an autonomous navigation method less susceptible to external environment, but due to the initial alignment declination and odometer coefficient error, navigation errors increase with time. In this paper, the map matching algorithm is used to precisely match the dead reckoning to compensate the vehicles heading deviation angle and odometer coefficient error by positioning error. Studies have shown that the odometer coefficient error and dynamic alignment accuracy are mainly affected by the vehicles displacement between two points and positioning accuracy. Error feedback iterative method is used to compensate for correction, which can better inhibit the gyro drift on navigation deviations impact. The simulation results show that considering the map matching algorithm accuracy under the premise of this program, the odometer coefficient error is significantly lower and the accuracy of alignment can reach minute level after five iterative calculations.

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