scholarly journals A New Filtering Algorithm for Generating Path Loss Model to Improve Accuracy of Trilateration-Based Indoor Positioning System

2021 ◽  
Vol 1962 (1) ◽  
pp. 012009
Author(s):  
L Susanto ◽  
W Wibisono ◽  
T Ahmad
Keyword(s):  
Path Loss ◽  
Indoor Positioning ◽  
Positioning System ◽  
Loss Model ◽  
Path Loss Model ◽  
Filtering Algorithm ◽  
Improve Accuracy ◽  
Indoor Positioning System

scholarly journals Trilateration Method For Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Herryawan Pujiharsono ◽  
Duwi Utami ◽  
Rafina Destiarti Ainul
Keyword(s):  
Path Loss ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Positioning System ◽  
Observation Area ◽  
Current Location ◽  
Zigbee Protocol ◽  
The Mean ◽  
Unknown Node ◽  
Indoor Positioning System

Wireless network technology that is used today is developing rapidly because of the increasing need for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a disadvantage of low accuracy of 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as an object and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate unknown node located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area is 0.9836 and the Mean Square Error of the test is 1.251 meters, which indicates that the system can be a solution to the indoor GPS problem.

scholarly journals THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 379-383
Author(s):  
J. Liu ◽  
C. Jiang ◽  
Z. Shi
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Localization Algorithm ◽  
External Device ◽  
Localization System ◽  
Filtering Algorithm ◽  
Coordinate Conversion ◽  
Indoor Positioning System

Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

scholarly journals Evaluation of Distance Error with Bluetooth Low Energy Transmission Model for Indoor Positioning

2021 ◽  
Author(s):  
Pichaya Supanakoon ◽  
Sathaporn Promwong
Keyword(s):  
Path Loss ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Low Energy ◽  
Location Based Services ◽  
Loss Model ◽  
Bluetooth Low Energy ◽  
Path Loss Model ◽  
Distance Error ◽  
Proposed Model

Currently, an indoor positioning is a challenge application for location-based services (LBS) and proximity-based services (PBS). However, the indoor channel has dense multipath fading, causing more distance error than outdoor positioning. In this paper, the distance error analysis model is proposed for indoor positioning. The indoor channel is modeled as the sum of path loss model and multipath fading model. The path loss model is a linear regression model (LRM) based on Friis’ transmission formula, used for estimating the distance from received signal strength (RSS). The multipath fading is a Gaussian statistical model with zero mean, used for characterizing the multipath fading effect. The normalized distance error is evaluated and defined. The indoor channel with Bluetooth low energy (BLE) beacons is measured and compared with the proposed model. From the results, the normalized distance error obtained from the proposed model corresponds very well to measurement. This proposed model can be used as a tool for designing an indoor positioning system to obtain the specific distance error.

scholarly journals RSS-based Indoor Positioning Using Convolutional Neural Network

2020 ◽  
Vol 16 (12) ◽  
pp. 82
Author(s):  
Safae El Abkari ◽  
Abdelilah Jilbab ◽  
Jamal El Mhamdi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Wireless Sensor ◽  
Positioning System ◽  
Improve Accuracy ◽  
Random Fluctuations ◽  
Indoor Positioning System

<p class="0abstract"><span lang="EN-US">Indoor Positioning has come under the spotlight in the last decade due to the increasing of location-based services demands. RSS Wi-Fi based positioning using the fingerprinting technique is widely used due to its low hardware requirements and simplicity. However, multi-path and fading cause random fluctuations of collected RSS values which affects the positioning accuracy. For this purpose, we propose an indoor positioning system based on RSS and convolutional neural network. This approach aims to improve accuracy by reducing the noise and the randomness of collected RSS values from a wireless sensor network. We implemented and evaluated our system using a single floor and multi-grid dataset. Our proposed approach provides a room and grid prediction accuracies of 100% and a mean error of location estimation of 0.98 m.</span></p>

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