scholarly journals Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments

2013 ◽  
Vol 12 (7) ◽  
pp. 1321-1334 ◽  
Author(s):  
Moustafa Seifeldin ◽  
Ahmed Saeed ◽  
Ahmed E. Kosba ◽  
Amr El-Keyi ◽  
Moustafa Youssef
Keyword(s):  
Large Scale ◽  
Localization System ◽  
Wireless Environments ◽  
Device Free ◽  
Passive Localization

scholarly journals LTE Signal Fingerprinting Device-Free Passive Localization in Changing Environments

2020 ◽  
Author(s):  
Ernestina Cianca ◽  
Giovanni Pecoraro ◽  
Mauro De Sanctis ◽  
Simone Di Domenico
Keyword(s):  
Experimental Results ◽  
Training Phase ◽  
Localization System ◽  
Environment Changes ◽  
Changing Environments ◽  
The One ◽  
Device Free ◽  
Passive Localization

This paper proposes a fingerprinting-based device Free Passive localization system based on the use of the LTE signal and it is robust to environment changes. The proposed methodology uses as fingerprints descriptors calculated on the CSI vectors rather than directly CSI vectors. The paper shows the performance of the proposed methods also assuming that the monitored environment might be different from the one characterized during the training phase as some equipment may be moved. Moreover, the paper compares the proposed method with signal fingerprinting approaches based on RSSI or direct CSI vectors. Experimental results, which consider one single LTE receiver in the monitored room, show the effectiveness of the proposed solution.

Synchronous Issue of IR-Based Large Scale Volume Localization System

2014 ◽  
Vol 496-500 ◽  
pp. 1643-1647
Author(s):  
Ying Feng Wu ◽  
Gang Yan Li
Keyword(s):  
Standard Deviation ◽  
Mobile Robot ◽  
Large Volume ◽  
Large Scale ◽  
Image Acquisition ◽  
Mean Value ◽  
Network Synchronization ◽  
Localization System ◽  
Localization Performance ◽  
The Mean

IR-based large scale volume localization system (LSVLS) can localize the mobile robot working in large volume, which is constituted referring to the MSCMS-II. Hundreds cameras in LSVLS must be connected to the control station (PC) through network. Synchronization of cameras which are mounted on different control stations is significant, because the image acquisition of the target must be synchronous to ensure that the target is localized precisely. Software synchronization method is adopted to ensure the synchronization of camera. The mean value of standard deviation of eight cameras mounted on two workstations is 12.53ms, the localization performance of LSVLS is enhanced.

A Large-Scale Indoor Layout Reconstruction and Localization System for Spatial-Aware Mobile AR Applications

2021 ◽  
Author(s):  
Kai-Wen Hsiao ◽  
Jheng-Wei Su ◽  
Yu-Chih Hung ◽  
Kuo-Wei Chen ◽  
Chih-Yuan Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Localization System ◽  
Mobile Ar

scholarly journals Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 52 ◽  
Author(s):  
Abdil Kaya ◽  
Stijn Denis ◽  
Ben Bellekens ◽  
Maarten Weyn ◽  
Rafael Berkvens
Keyword(s):  
Control System ◽  
Radio Frequency ◽  
Large Scale ◽  
Reference Data ◽  
Added Value ◽  
Size Estimation ◽  
Music Festival ◽  
Large Scale Dataset ◽  
Access Control System ◽  
Device Free

Organisers of events attracting many people have the important task to ensure the safety of the crowd on their venue premises. Measuring the size of the crowd is a critical first step, but often challenging because of occlusions, noise and the dynamics of the crowd. We have been working on a passive Radio Frequency (RF) sensing technique for crowd size estimation, and we now present three datasets of measurements collected at the Tomorrowland music festival in environments containing thousands of people. All datasets have reference data, either based on payment transactions or an access control system, and we provide an example analysis script. We hope that future analyses can lead to an added value for crowd safety experts.

scholarly journals MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1645 ◽  
Author(s):  
Ryota Kimoto ◽  
Shigemi Ishida ◽  
Takahiro Yamamoto ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Single Channel ◽  
Sensor Nodes ◽  
Localization Problem ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Localization System ◽  
Channel Diversity ◽  
Radio Signals

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

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