scholarly journals Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5707
Author(s):  
Ching-Han Chen ◽  
Pi-Wei Chen ◽  
Pi-Jhong Chen ◽  
Tzung-Hsin Liu
Keyword(s):  
Magnetic Field ◽  
Sensor Array ◽  
Probabilistic Neural Network ◽  
Indoor Positioning ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
The Magnetic Field

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

scholarly journals Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Wenhua Shao ◽  
Fang Zhao ◽  
Cong Wang ◽  
Haiyong Luo ◽  
Tunio Muhammad Zahid ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Feature Extraction ◽  
Measurement Technique ◽  
Extraction Methods ◽  
Indoor Positioning ◽  
Coarse Grained ◽  
Electronic Noise ◽  
Magnetic Field Magnitude ◽  
Positioning Method ◽  
The Magnetic Field

Smartphone based indoor positioning has greatly helped people in finding their positions in complex and unfamiliar buildings. One popular positioning method is by utilizing indoor magnetic field, because this feature is stable and infrastructure-free. In this method, the magnetometer embedded on the smartphone measures indoor magnetic field and queries its position. However, the environments of the magnetometer are rather harsh. This harshness mainly consists of coarse-grained hard/soft-iron calibrations and sensor electronic noise. The two kinds of interferences decrease the position distinguishability of the magnetic field. Therefore, it is important to extract location features from magnetic fields to reduce these interferences. This paper analyzes the main interference sources of the magnetometer embedded on the smartphone. In addition, we present a feature distinguishability measurement technique to evaluate the performance of different feature extraction methods. Experiments revealed that selected fingerprints will improve position distinguishability.

Magnetic Emission Mapping for Passive Integrated Components Characterisation

2003 ◽  
Author(s):  
O. Crépel ◽  
Y. Bouttement ◽  
P. Descamps ◽  
C. Goupil ◽  
P. Perdu ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Mobile Phone ◽  
Printed Circuit Board ◽  
Magnetic Sensors ◽  
Circuit Board ◽  
Magnetic Disturbance ◽  
Passive Components ◽  
Printed Circuit ◽  
Integrated Inductor ◽  
The Magnetic Field

Abstract We developed a system and a method to characterize the magnetic field induced by circuit board and electronic component, especially integrated inductor, with magnetic sensors. The different magnetic sensors are presented and several applications using this method are discussed. Particularly, in several semiconductor applications (e.g. Mobile phone), active dies are integrated with passive components. To minimize magnetic disturbance, arbitrary margin distances are used. We present a system to characterize precisely the magnetic emission to insure that the margin is sufficient and to reduce the size of the printed circuit board.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

scholarly journals A Feasibility Study of a Noncontact Torque Sensor with Multiple Hall Sensors

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Kyungshik Lee ◽  
Chongdu Cho
Keyword(s):  
Magnetic Field ◽  
Permanent Magnets ◽  
Power Transmission ◽  
Magnetic Sensors ◽  
Torque Sensor ◽  
Torque Measurement ◽  
Test Range ◽  
Variable Power ◽  
Hall Sensors ◽  
The Magnetic Field

The feasibility of a noncontact sensor is investigated. This type of sensor can potentially be used for torque measurement in a speed-variable power transmission system. Torque can be read by examining the phase difference between two induction signals from respective magnetic sensors that detect the magnetic field intensity of permanent magnets mounted on the surface of a shaft in rotation. A real-time measuring algorithm that includes filtering and calibration is adopted to measure the torque magnitude. It is shown that this new torque sensor can perform well under rotation speeds ranging from 300 rpm to 500 rpm. As an interim report rather than a complete development, this work demonstrates the feasibility of noncontact torque measurement by monitoring a magnetic field. The result shows an error of less than 2% within the full test range, which is a sufficient competitive performance for commercial sensors. The price is very low compared to competitors in the marketplace, and the device does not require special handling of the shaft of the surface.

scholarly journals An Indoor Positioning System Using Multiple Methods and Tools

2018 ◽  
Vol 4 (1) ◽  
pp. 11-22
Author(s):  
Michael Adeyeye Oshin ◽  
Nobaene Sehloho
Keyword(s):  
Wireless Mesh Network ◽  
Ad Hoc ◽  
Indoor Positioning ◽  
Mesh Network ◽  
Positioning System ◽  
Tracking Systems ◽  
Positioning Systems ◽  
Wireless Mesh ◽  
Positioning Method ◽  
Indoor Positioning System

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.

scholarly journals Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2704 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Field Data ◽  
Performance Comparison ◽  
Magnetic Sensors ◽  
Location Based Services ◽  
Field Pattern ◽  
Magnetic Field Data ◽  
Localization Performance ◽  
The Magnetic Field

Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.

scholarly journals A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

2020 ◽  
Vol 10 (3) ◽  
pp. 956 ◽  
Author(s):  
Minghao Si ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Meng Sun ◽  
Hongji Cao
Keyword(s):  
Numerical Experiments ◽  
New Technologies ◽  
Gaussian Model ◽  
Indoor Positioning ◽  
Least Square ◽  
Line Of Sight ◽  
Positioning Accuracy ◽  
Timing Measurement ◽  
Positioning Method ◽  
Non Line Of Sight

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

Experimental Study Noise of HMR2003 Sensor with or without the Use of Amplifier AMP04

2014 ◽  
Vol 605 ◽  
pp. 629-632 ◽  
Author(s):  
N. Hadjigeorgiou
Keyword(s):  
Magnetic Field ◽  
Experimental Study ◽  
Angular Velocity ◽  
Rotation Angle ◽  
Magnetic Sensors ◽  
Successful Attempt ◽  
Wear Measurement ◽  
The Magnetic Field

Magnetic sensors offer many essential benefits: they enable contactless and thus without wear measurement of mechanical amounts of such as the rotation angle and angular velocity. And are a powerful and economical solution. In this work became a successful attempt to detect and record the noise of a Anisotropic Magnetoresistors (AMR) sensor, hte HMC2003, which manufactured by Honeywell Inc. was tested for its ability to detect the magnetic field and as well as how the corruption, which are involved due to the noise.

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