scholarly journals A Clustering-Based Approach for Improving the Accuracy of UWB Sensor-Based Indoor Positioning System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Taner Arsan ◽  
Mohammed Muwafaq Noori Hameez
Keyword(s):  
Kalman Filter ◽  
Mean Shift ◽  
Indoor Positioning ◽  
Optimal Number ◽  
Ultra Wideband ◽  
Positioning System ◽  
Indoor Location ◽  
Test Points ◽  
The Impact ◽  
Indoor Positioning System

There are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system.

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

scholarly journals STATISTICAL ANALYSIS OF WSN BASED INDOOR POSITIONING LOCALIZATION SCHEMES WITH KALMAN FILTERING

2012 ◽  
pp. 36-41
Author(s):  
A.MOHAMED RIAS ◽  
R.SAMBATH KUMAR ◽  
G.SATHISH KUMAR ◽  
A. SIVAGAMI
Keyword(s):  
Sensor Network ◽  
Indoor Positioning ◽  
Positioning System ◽  
Shopping Malls ◽  
Network Localization ◽  
Real Time Tracking ◽  
The Impact ◽  
Indoor Positioning System ◽  
Tracking Of Humans ◽  
Installation Cost

Wireless Sensor Network (WSN) is used for determining the Indoor Positioning of objects and persons since recent years. WSN has been implemented in indoor positioning applications such as real time tracking of humans/objects, patient monitoring in health care, navigation, warehouses for inventory monitoring, shopping malls, etc. But one of the problems while implementing WSN in Indoor positioning system is to ensure more coverage large number of sensors must be deployed which increases the installation cost. So in this paper, we have used MATLAB GUI named Sensor Network Localization Explorer to analyze the impact of node density on indoor positioning localization schemes. Later we have integrated the Kalman filter with the indoor positioning system to increase the reliability and reduce the localization error of the system with lesser number of nodes.

scholarly journals A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3766 ◽  
Author(s):  
Soumya Rana ◽  
Javier Prieto ◽  
Maitreyee Dey ◽  
Sandra Dudley ◽  
Juan Corchado
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Positioning System ◽  
Indoor Location ◽  
Indoor Scenarios ◽  
Indoor Location Systems ◽  
Indoor Positioning System

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

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