An implementation of positioning system in indoor environment based on active RFID

2009 ◽  
Author(s):  
Ching-Sheng Wang ◽  
Chun-Hong Huang ◽  
Yong-Si Chen ◽  
Li-Jie Zheng
Keyword(s):  
Indoor Environment ◽  
Positioning System ◽  
Active Rfid

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 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.

scholarly journals Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1496 ◽  
Author(s):  
Muhammad Ali ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Indoor Environment ◽  
Automatic Generation ◽  
Indoor Positioning ◽  
Propagation Loss ◽  
Positioning System ◽  
Site Analysis ◽  
Raster Maps ◽  
Average Accuracy ◽  
Online Feedback ◽  
Indoor Positioning System

Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.

scholarly journals Calibration of Beacons for Indoor Environments based on a Digital Map and Heuristic Information

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 670
Author(s):  
David Gualda ◽  
Jesús Ureña ◽  
José Alcalá ◽  
Carlos Santos
Keyword(s):  
Genetic Algorithm ◽  
Indoor Environment ◽  
Harmony Search ◽  
Positioning System ◽  
Indoor Environments ◽  
Angle Of Incidence ◽  
Distance Measurements ◽  
Digital Map ◽  
Local Positioning System ◽  
Numerical Minimization

This paper proposes an algorithm for calibrating the position of beacons which are placed on the ceiling of an indoor environment. In this context, the term calibration is used to estimate the position coordinates of a beacon related to a known reference system in a map. The positions of a set of beacons are used for indoor positioning purposes. The operation of the beacons can be based on different technologies such as radiofrequency (RF), infrared (IR) or ultrasound (US), among others. In this case we are interested in the positions of several beacons that compose an Ultrasonic Local Positioning System (ULPS) placed on different strategic points of the building. The calibration proposal uses several distances from a beacon to the neighbor walls measured by a laser meter. These measured distances, the map of the building in a vector format and other heuristic data (such as the region in which the beacon is located, the approximate orientation of the distance measurements to the walls and the equations in the map coordinate system of the line defining these walls) are the inputs of the proposed algorithm. The output is the best estimation of the position of the beacon. The process is repeated for all the beacons. To find the best estimation of the position of the beacons we have implemented a numerical minimization based on the use of a Genetic Algorithm (GA) and a Harmony Search (HS) methods. The proposal has been validated with simulations and real experiments, obtaining the positions of the beacons and an estimation of the error associated that depends on which walls (and the angle of incidence of the laser) are selected to make the distance measurements.

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