scholarly journals VLC-Based Positioning System for an Indoor Environment Using an Image Sensor and an Accelerometer Sensor

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 783 ◽  
Author(s):  
Phat Huynh ◽  
Myungsik Yoo
Keyword(s):  
Indoor Environment ◽  
Image Sensor ◽  
Positioning System ◽  
Accelerometer Sensor

scholarly journals An Indoor AC Magnetic Positioning System

2021 ◽  
Author(s):  
Paolo Carbone ◽  
Guido De Angelis ◽  
Valter Pasku ◽  
Alessio De Angelis ◽  
Marco Dionigi ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning ◽  
Experimental Results ◽  
System Level ◽  
Line Of Sight ◽  
Positioning System ◽  
Positioning Error ◽  
Geometric Configurations ◽  
Indoor Positioning System ◽  
Non Line Of Sight

<div><div><div><p>This paper describes the design and realization of a Magnetic Indoor Positioning System. The system is entirely realized using off-the-shelf components and is based on inductive coupling between resonating coils. Both system-level architecture and realization details are described along with experimental results. The realized system exhibits a maximum positioning error of less than 10 cm in an indoor environment over a 3×3 m2 area. Extensive experiments in larger areas, in non-line-of-sight conditions, and in unfavorable geometric configurations, show sub-meter accuracy, thus validating the robustness of the system with respect to other existing solutions.</p></div></div></div>

scholarly journals Indoor Positioning System using LED Lights and a Dual Image Sensor

2015 ◽  
Vol 19 (6) ◽  
pp. 586-591 ◽  
Author(s):  
Myoung-geun Moon ◽  
Su-il Choi ◽  
Jaehyung Park ◽  
Jin Young Kim
Keyword(s):  
Indoor Positioning ◽  
Image Sensor ◽  
Positioning System ◽  
Dual Image ◽  
Led Lights ◽  
Indoor Positioning System

scholarly journals Real-Time 3D UAV Pose Estimation by Visualization

Proceedings ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 18
Author(s):  
Nenchoo ◽  
Tantrairatn
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Indoor Environment ◽  
Target Object ◽  
Outdoor Environment ◽  
Visual Object ◽  
Depth Camera ◽  
Positioning System ◽  
Stereo Camera ◽  
Local Positioning System

This paper presents an estimation of 3D UAV position in real-time condition by using Intel RealSense Depth camera D435i with visual object detection technique as a local positioning system for indoor environment. Nowadays, global positioning system or GPS is able to specify UAV position for outdoor environment. However, for indoor environment GPS hasn’t a capability to determine UAV position. Therefore, Depth stereo camera D435i is proposed to observe on ground to specify UAV position for indoor environment instead of GPS. Using deep learning for object detection to identify target object with depth camera to specifies 2D position of target object. In addition, depth position is estimated by stereo camera and target size. For experiment, Parrot Bebop2 as a target object is detected by using YOLOv3 as a real-time object detection system. However, trained Fully Convolutional Neural Networks (FCNNs) model is considerably significant for object detection, thus the model has been trained for bebop2 only. To conclude, this proposed system is able to specifies 3D position of bebop2 for indoor environment. For future work, this research will be developed and apply for visualized navigation control of drone swarm.

scholarly journals Preliminary Analysis of Organic Wireless Positioning System in an Indoor Environment

2019 ◽  
Vol 705 ◽  
pp. 012032
Author(s):  
A H Ismail ◽  
J S Ngo ◽  
M I Taib ◽  
M S M Hashim ◽  
M S M Azmi ◽  
...  
Keyword(s):  
Preliminary Analysis ◽  
Indoor Environment ◽  
Positioning System ◽  
Wireless Positioning

scholarly journals DEVELOPMENT OF LOCATION ESTIMATION ALGORITHM UTILIZING RSSI FOR LORA POSITIONING SYSTEM

2021 ◽  
Vol 84 (1) ◽  
pp. 97-105
Author(s):  
S. Kavetha ◽  
A. S. Ja'afar ◽  
M. Z. A. Aziz ◽  
A. A. M. Isa ◽  
M. S. Johal ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Low Power ◽  
Indoor Environment ◽  
Multipath Fading ◽  
Location Estimation ◽  
Line Of Sight ◽  
Area Network ◽  
Positioning System ◽  
Localization Algorithm ◽  
Wide Area Network

LoRa is identified as Long-Range low power network technology for Low Power Wide Area Network (LPWAN) usage. Nowadays, Global Positioning System (GPS) is an important system which is used for location and navigation predominantly used in outdoor but less accurate in indoor environment. Most of LoRa technology have been used on the internet-of-things (ioT) but very few use it as localization system. In this project, a GPS-less solution is proposed where LoRa Positioning System was developed which consists of LoRa transmitter, LoRa transceiver and LoRa receiver. The system has been developed by collecting the RSSI which is then used for the distance estimation. Next, Kalman filter with certain model has been implemented to overcome the effect of multipath fading especially for indoor environment and the trilateration technique is applied to estimate the location of the user. Both distribution estimation results for Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) condition were analyzed. Then, the comparison RMSE achievement is analyzed between the trilateration and with the Kalman Filter. GPS position also were collected as comparison to the LoRa based positioning. Lastly, the Cumulative Density Function (CDF) shows 90% of the localization algorithm error for LOS is lower than 0.82 meters while for NLOS is 1.17 meters.

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