scholarly journals A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

2021 ◽  
Vol 10 (7) ◽  
pp. 442
Author(s):  
Da Li ◽  
Zhao Niu
Keyword(s):  
Wavelet Transform ◽  
Positioning System ◽  
Fingerprint Image ◽  
Positioning Accuracy ◽  
Location Services ◽  
Multiple Data ◽  
Signal Features ◽  
Outdoor Environments ◽  
Positioning Method ◽  
Fingerprint Database

As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.

scholarly journals Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongbin Pan ◽  
Yang Xiang ◽  
Jian Xiong ◽  
Yifan Zhao ◽  
Ziwei Huang ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Detection Method ◽  
Access Point ◽  
Combination Method ◽  
Data Set ◽  
Positioning Accuracy ◽  
Multipath Effects ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Mismatch Rate

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

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 Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 67 ◽  
Author(s):  
Fernando J. Aranda ◽  
Felipe Parralejo ◽  
Fernando J. Álvarez ◽  
Joaquín Torres-Sospedra
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Low Energy ◽  
Location Based Services ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Bluetooth Low Energy ◽  
Outdoor Environments ◽  
The University

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach.

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 An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4420 ◽  
Author(s):  
Lu Huang ◽  
Xingli Gan ◽  
Baoguo Yu ◽  
Heng Zhang ◽  
Shuang Li ◽  
...  
Keyword(s):  
Carrier Phase ◽  
Indoor Environment ◽  
Satellite System ◽  
Indoor Positioning ◽  
Integer Ambiguity ◽  
Positioning System ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Static Conditions ◽  
Fingerprint Database

Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms.

The Study Based on LABVIEW of the Positioning Accuracy of Spinneret Microporous Fine Machine

2013 ◽  
Vol 712-715 ◽  
pp. 1347-1350
Author(s):  
Si Yu Liu ◽  
Pei Feng ◽  
Chong Chang Yang
Keyword(s):  
Pore Size ◽  
High Precision ◽  
Vital Role ◽  
Positioning System ◽  
Precision Positioning ◽  
Positioning Accuracy ◽  
Small Pore Size ◽  
Small Pore ◽  
Positioning Method

The microporous on spinneret has the characteristics of high precision, small pore size and large quantity. Spinneret microporous finishing operation is mainly used for removing the burr formed on the spinneret silk surface after micropores drilling and punching. The burrs seriously affect the quality of the spinning fibers. Micropore spinneret finishing plays a vital role in improving the quality of processing micropore. This paper introduces a system consisting of the spinneret micropore fine machine,then focuses on fine machine positioning system and proposes a high-precision positioning method of the microporous.

scholarly journals A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3702 ◽  
Author(s):  
Hui-Seon Gang ◽  
Jae-Young Pyun
Keyword(s):  
Indoor Positioning ◽  
Location Based Services ◽  
Measurement Unit ◽  
Service Needs ◽  
Positioning System ◽  
Indoor Environments ◽  
Experimental Conditions ◽  
Daily Lives ◽  
Location Services ◽  
Positioning Method

As smartphone built-in sensors, wireless technologies, and processor computing power become more advanced and global positioning system (GPS)-based positioning technologies are improving, location-based services (LBS) have become a part of our daily lives. At the same time, demand has grown for LBS applications in indoor environments, such as indoor path finding and navigation, marketing, entertainment, and location-based information retrieval. In this paper, we demonstrate the design and implementation of a smartphone-based indoor LBS system for location services consisting of smartphone applications and a server. The proposed indoor LBS system uses hybrid indoor positioning methods based on Bluetooth beacons, Geomagnetic field, Inertial Measurement Unit (IMU) sensors, and smartphone cameras and can be used for three types of indoor LBS applications. The performance of each positioning method demonstrates that our system retains the desired accuracy under experimental conditions. As these results illustrate that our system can maintain positioning accuracy to within 2 m 80% of the time, we believe our system can be a real solution for various indoor positioning service needs.

scholarly journals Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

2020 ◽  
Vol 9 (12) ◽  
pp. 714
Author(s):  
Yankun Wang ◽  
Renzhong Guo ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Indoor Positioning ◽  
Kriging Interpolation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Kriging Interpolation Method ◽  
Additional Equipment ◽  
Positioning Method ◽  
Fingerprint Database

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

scholarly journals A novel method of adaptive weighted K-nearest neighbor fingerprint indoor positioning considering user’s orientation

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878588 ◽  
Author(s):  
Jingxue Bi ◽  
Yunjia Wang ◽  
Xin Li ◽  
Hongji Cao ◽  
Hongxia Qi ◽  
...  
Keyword(s):  
Human Body ◽  
Nearest Neighbor ◽  
Reference Points ◽  
Average Error ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Factors Affecting ◽  
Positioning Method ◽  
Hybrid Distance ◽  
Fingerprint Database

There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K-nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c-means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K-reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K-nearest neighbor fingerprint positioning method can improve positioning accuracy.

Research on Indoor 3D Location Technology Based on RSSI

2014 ◽  
Vol 696 ◽  
pp. 241-246 ◽  
Author(s):  
Bo Xin Mao ◽  
Shan Liu ◽  
Jian Ping Chai
Keyword(s):  
Real Time ◽  
Global Positioning System ◽  
Mobile Communication ◽  
Rapid Development ◽  
Three Dimensional ◽  
Propagation Model ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Global Positioning ◽  
Positioning Method

With the rapid development of mobile communication, the GPS (Global Positioning System) which provides real-time global positioning system has not been able to meet the needs of the indoor accurate positioning. Through simulation, this paper implements the method of indoor three-dimensional positioning based on RSSI compared the positioning accuracy under several kinds of noise. We achieve the good indoor three-dimensional positioning method with the combination of cost, positioning accuracy and positioning precision through the filter and secondary positioning which establishes special propagation model for various different environments.

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