scholarly journals A Small World Graph Approach for an Efficient Indoor Positioning System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5013
Author(s):  
Max Lima ◽  
Leonardo Guimarães ◽  
Eulanda Santos ◽  
Edleno Moura ◽  
Rafael Costa ◽  
...  
Keyword(s):  
Large Scale ◽  
Primary Source ◽  
Small World ◽  
Indoor Positioning ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Positioning System ◽  
Indoor Environments ◽  
Indoor Positioning System ◽  
Small World Graph

The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this purpose, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample that needs to be classified must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large-scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, large-scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 98% when compared to the classic kNN and at least 80% when compared to tree-based approaches.

scholarly journals Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3657 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Environments ◽  
University Campus ◽  
Sources Of Information ◽  
Visual Place Recognition ◽  
Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

scholarly journals Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5776
Author(s):  
Zhongfeng Zhang ◽  
Minjae Lee ◽  
Seungwon Choi
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Software Radio ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Training Dataset ◽  
Positioning System ◽  
Indoor Environments ◽  
Generative Adversarial Network ◽  
Indoor Positioning System

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

WiFi and Bluetooth Low Energy Beacons for Indoor Positioning System

2019 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Nurul Fatehah Zulkpli ◽  
Nor Azlina Ab. Aziz ◽  
Noor Ziela Abd Rahman ◽  
Rosli Besar
Keyword(s):  
Indoor Positioning ◽  
Low Energy ◽  
Location Based Services ◽  
Positioning System ◽  
Indoor Environments ◽  
Wireless Technologies ◽  
Bluetooth Low Energy ◽  
Advantages And Disadvantages ◽  
Performance Accuracy ◽  
Indoor Positioning System

Indoor Positioning System (IPS) is used to locate a person, an object or a location inside a building. IPS is important in providing location-based services, which has recently gain much popularity. The services ease visitors’ navigation at unfamiliar premises. Location-based services depend on the capability of IPS to accurately determine the location of the user, which is a challenging issue in indoor environments. Several wireless technologies are available. In this paper, two of the most widely used IPS technologies are reviewed which are, WiFi and Bluetooth low energy (BLE). Their advantages and disadvantages are reviewed and reported here. Comparison of the systems based on their performance, accuracy and limitations are presented as well.

scholarly journals Semi-Autonomous Coordinate Configuration Technology of Base Stations in Indoor Positioning System Based on UWB

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Xiaofei Yang ◽  
Jun Wang ◽  
Hui Ye ◽  
Jianzhen Li
Keyword(s):  
Large Scale ◽  
Target Location ◽  
Wide Band ◽  
Indoor Positioning ◽  
Base Station ◽  
Base Stations ◽  
Positioning System ◽  
Linear Network ◽  
Gps System ◽  
Indoor Positioning System

In the global positioning system (GPS) denied environment, an indoor positioning system based on ultra-wide band (UWB) technology has been utilized for target location and navigation. It can provide a more accurate positioning measurement than those based on received signal strength (RSS). Although promising, it suffers from some shortcomings that base stations should be preinstalled to obtain reference coordinate information, just as navigation satellites in the GPS system. In order to improve the positioning accuracy, a large number of base stations should be preinstalled and assigned coordinates in the large-scale network. However, the coordinate setup process of the base stations is cumbersome, time consuming, and laborious. For a class of linear network topology, a semi-autonomous coordinate configuration technology of base stations is designed, which refers to three conceptions of segmentation, virtual triangle, and bidirectional calculation. It consists of two stages in every segment: Forward and backward. In the forward stage, it utilizes the manual coordinate setup method to deal with the foremost two base stations, and then the remaining base stations autonomously calculate their coordinates by building the virtual triangle train. In the backward stage, the reverse operation is performed, but the foremost two base stations of the next segment should be used as the head. In the last segment, the last two base stations should be used as the head. Integrating forward and backward data, the base stations could improve their location accuracy. It is shown that our algorithm is feasible and practical in simulation results and can dramatically reduce the system configuration time. In addition, the error and maximum base station number for one segment caused by our algorithm are discussed theoretically.

A New Indoor Positioning System Using Artificial Encoded Magnetic Fields

2017 ◽  
Vol 71 (2) ◽  
pp. 299-316 ◽  
Author(s):  
Falin Wu ◽  
Yuan Liang ◽  
Yong Fu ◽  
Chenghao Geng
Keyword(s):  
Magnetic Fields ◽  
Three Dimensional ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Positioning System ◽  
Indoor Environments ◽  
Step Method ◽  
Satellite Systems ◽  
Multipath Effects ◽  
Indoor Positioning System

The demand for accurate indoor positioning continues to grow but the predominant positioning technologies such as Global Navigation Satellite Systems (GNSS) are not suitable for indoor environments due to multipath effects and Non-Line-Of-Sight (NLOS) conditions. This paper presents a new indoor positioning system using artificial encoded magnetic fields, which has good properties for NLOS conditions and fewer multipath effects. The encoded magnetic fields are generated by multiple beacons; each beacon periodically generates unique magnetic field sequences, which consist of a gold code sequence and a beacon location sequence. The position of an object can be determined with measurements from a tri-axial magnetometer using a three-step method: performing time synchronisation between sensor and beacons, identifying the beacon field and the beacon location, and estimating the position of the object. The results of the simulation and experiment show that the proposed system is capable of achieving Two-Dimensional (2D) and Three-Dimensional (3D) accuracy at sub-decimetre and decimetre levels, respectively.

Amp-Phi: A CSI-Based Indoor Positioning System

2018 ◽  
Vol 32 (09) ◽  
pp. 1858005 ◽  
Author(s):  
Tao-Yun Zhou ◽  
Bao-Wang Lian ◽  
Yi Zhang ◽  
Sen Liu
Keyword(s):  
Nearest Neighbor ◽  
Phase Error ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Positioning System ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Error Mitigation ◽  
Indoor Positioning System

With rapid growth in the demand of location-based services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interest due to their convenience. Until now, most such methods were based on received signal strength indicator (RSSI), which is vulnerable to non-line-of-sight (NLOS). In order to realize high-precision indoor positioning, we propose a channel state information (CSI)-based Amp-Phi indoor-positioning system which exploits the amplitude and phase information of CSI at the same time to establish a fingerprinting database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build an NLOS mitigation model and a phase error mitigation model, respectively, to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and machine learning (ML) systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, by doctors or patients in the hospital, for people localization in large supermarkets or museums and so on.

scholarly journals Unsupervised Indoor Positioning System Based on Environmental Signatures

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 327
Author(s):  
Pan Feng ◽  
Danyang Qin ◽  
Min Zhao ◽  
Ruolin Guo ◽  
Teklu Berhane
Keyword(s):  
Dead Reckoning ◽  
Indoor Positioning ◽  
Convergence Time ◽  
Positioning System ◽  
Indoor Environments ◽  
Test Results ◽  
Positioning Errors ◽  
Localization And Mapping ◽  
Indoor Positioning System ◽  
Unconventional Method

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.

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