scholarly journals An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 130708-130718
Author(s):  
Teng Tan ◽  
Lingwen Zhang ◽  
Qiumei Li
Keyword(s):  
Indoor Localization ◽  
Matrix Completion ◽  
Database Construction ◽  
Fingerprint Database

scholarly journals Indoor Floor Localization Based on Multi-Intelligent Sensors

2020 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Min Zhao ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Xinxin Wang
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Collaborative Work ◽  
Magnetic Data ◽  
Data Mapping ◽  
Localization Accuracy ◽  
Database Construction ◽  
Intelligent Sensors ◽  
Majority Principle ◽  
Fingerprint Database

With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL.

scholarly journals Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2990 ◽  
Author(s):  
Junhong Lin ◽  
Bang Wang ◽  
Guang Yang ◽  
Mu Zhou
Keyword(s):  
Indoor Localization ◽  
Field Measurements ◽  
Localization Accuracy ◽  
Database Construction ◽  
Cross Domain ◽  
Site Survey ◽  
Intersection Algorithm ◽  
Promising Solution ◽  
Fingerprint Database ◽  
Sample Annotation

Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.

scholarly journals Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

2022 ◽  
Vol 4 ◽  
pp. 167-189
Author(s):  
Dwi Joko Suroso ◽  
Farid Yuli Martin Adiyatma ◽  
Panarat Cherntanomwong ◽  
Pitikhate Sooraksa
Keyword(s):  
Indoor Localization ◽  
Polynomial Regression ◽  
Polynomial Interpolation ◽  
Received Signal Strength Indicator ◽  
Database Construction ◽  
3D Environments ◽  
Regression Techniques ◽  
Fingerprint Database ◽  
2D And 3D ◽  
Classical Interpolation

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

scholarly journals Wi-fi fingerprint database construction using Chebyshev wavelet functions

2015 ◽  
Author(s):  
Anvar Narzullaev ◽  
Azamjon Nemadaliev ◽  
Hasan Selamat Mohd ◽  
Mohamed Othman ◽  
Khaironi Yatim Sharif
Keyword(s):  
Database Construction ◽  
Chebyshev Wavelet ◽  
Fingerprint Database

scholarly journals mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2283 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Flux ◽  
Indoor Localization ◽  
Search Space ◽  
Initial Position ◽  
Received Signal Strength Indicator ◽  
Strength Based ◽  
Flux Intensity ◽  
Smartphone Sensors ◽  
Path Lengths ◽  
Fingerprint Database

An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fingerprinting. The down side is the need to update the database periodically and device heterogeneity. We solve this problem by using the fingerprint database of patterns formed by magnetic flux intensity values. The pattern matching approach solves the problem of device heterogeneity and the algorithm’s performance with Samsung Galaxy S8 and LG G6 is comparable. A deep learning based artificial neural network is adopted to identify the user state of walking and stationary and its accuracy is 95%. The localization is totally infrastructure independent and does not require any other technology to constraint the search space. The experiments are performed to determine the accuracy in three buildings of Yeungnam University, Republic of Korea with different path lengths and path geometry. The results demonstrate that the error is 2–3 m for 50 percentile with various buildings. Even though many locations in the same building exhibit very similar magnetic attitude, the algorithm achieves an accuracy of 4 m for 75 percentile irrespective of the device used for localization.

scholarly journals An efficient crowd-sourcing-based approach for fingerprint database updating

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985851
Author(s):  
Xi Liu ◽  
Jian Cen ◽  
Yiju Zhan ◽  
Chengpei Tang
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Primary Concern ◽  
Indoor Environments ◽  
Crowd Sourcing ◽  
Static Data ◽  
Decision Methods ◽  
Fingerprint Database ◽  
Update Frequency

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

Chameleon: Survey-Free Updating of a Fingerprint Database for Indoor Localization

2016 ◽  
Vol 15 (4) ◽  
pp. 66-75 ◽  
Author(s):  
Suining He ◽  
Bo Ji ◽  
S.-H. Gary Chan
Keyword(s):  
Indoor Localization ◽  
Fingerprint Database
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