Cooperatively extending the range of indoor localisation

2013 ◽  
Author(s):  
G. Cullen ◽  
J. Santos ◽  
K. Curran
Keyword(s):  
Indoor Localisation

Viewing sector optimisation for indoor localisation using multi-lateration with unilateral nodes deployment

2019 ◽  
Vol 9 (3) ◽  
pp. 151-157 ◽  
Author(s):  
Enrique Gonzalez-Zepeda ◽  
Yuan Xu ◽  
Yuriy S. Shmaliy ◽  
Jose A. Andrade-Lucio
Keyword(s):  
Indoor Localisation

scholarly journals SolarSLAM: Battery-free Loop Closure for Indoor Localisation

2020 ◽  
Author(s):  
Bo Wei ◽  
Weitao Xu ◽  
Chengwen Luo ◽  
Guillaume Zoppi ◽  
Dong Ma ◽  
...  
Keyword(s):  
Loop Closure ◽  
Indoor Localisation

scholarly journals WiFiNet: WiFi-based indoor localisation using CNNs

2021 ◽  
pp. 114906
Author(s):  
Noelia Hernández ◽  
Ignacio Parra ◽  
Héctor Corrales ◽  
Rubén Izquierdo ◽  
Augusto Luis Ballardini ◽  
...  
Keyword(s):  
Indoor Localisation

scholarly journals Automatic Detection of Missing Access Points in Indoor Positioning System †

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3595 ◽  
Author(s):  
Rafał Górak ◽  
Marcin Luckner
Keyword(s):  
Automatic Detection ◽  
Significant Loss ◽  
Classification Error ◽  
Positioning System ◽  
Random Forest Algorithm ◽  
Access Points ◽  
The Mean ◽  
Academic Building ◽  
Indoor Positioning System ◽  
Indoor Localisation

The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

scholarly journals Sensor Modalities and Fusion for Robust Indoor Localisation

2019 ◽  
Vol 6 (18) ◽  
pp. 162670
Author(s):  
Michał Kozłowski ◽  
Raúl Santos-Rodríguez ◽  
Robert Piechocki
Keyword(s):  
Indoor Localisation

scholarly journals Ultra-wideband-based multilateration technique for indoor localisation

2014 ◽  
Vol 8 (10) ◽  
pp. 1800-1809 ◽  
Author(s):  
Oladimeji Onalaja ◽  
Mounir Adjrad ◽  
Mohammad Ghavami
Keyword(s):  
Ultra Wideband ◽  
Indoor Localisation
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