| Title: |
Impact of calibration on indoor positionning precision |
| Authors: |
Tolza, Xavier; Acco, Pascal; Fourniols, Jean-Yves |
| Contributors: |
Équipe Instrumentation embarquée et systèmes de surveillance intelligents (LAAS-S4M); Laboratoire d'analyse et d'architecture des systèmes (LAAS); Université Toulouse Capitole (UT Capitole); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse); Institut National des Sciences Appliquées (INSA)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut National des Sciences Appliquées (INSA)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse - Jean Jaurès (UT2J); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse III - Paul Sabatier (UT3); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse Capitole (UT Capitole); Communauté d'universités et établissements de Toulouse (Comue de Toulouse) |
| Source: |
International Conference on Indoor Positioning and Indoor Navigation (IPIN), ; https://laas.hal.science/hal-01887434 ; International Conference on Indoor Positioning and Indoor Navigation (IPIN),, Sep 2018, Nantes, France. 4p |
| Publisher Information: |
CCSD |
| Publication Year: |
2018 |
| Collection: |
Université Toulouse III - Paul Sabatier: HAL-UPS |
| Subject Terms: |
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; [NLIN.NLIN-SI]Nonlinear Sciences [physics]/Exactly Solvable and Integrable Systems [nlin.SI]; [STAT.AP]Statistics [stat]/Applications [stat.AP] |
| Subject Geographic: |
Nantes; France |
| Description: |
International audience ; Internet of Things (IOT) business has raised a strong interest in assets positioning. While outdoor positioning mostly uses a Global Navigation Satellite System (GNNS) system, those are inoperable in indoor situations. Indoor positioning has been a very active field of study for the last decades and many approaches have been proposed, however those solutions are very often complex to deploy at industrial grade, due to complexity or cost of the hardware or the algorithms. Moreover, deploying those solution often requires a complex and expensive calibration setup such as fingerprinting to establish a radio map of the room. In this study, we focus on Bluetooth Low Energy (BLE) Received Signal Strength Indication (RSSI) positioning using maximum likelihood estimate and Cramer-Ro Lower Bound (CRLB) with the widely used Log-Distance Path Loss (LDPL) model to determine the impact of the calibration setup on the accuracy and precision of the position. Results are then matched with real BLE measurements made in an industrial-like environment. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://laas.hal.science/hal-01887434; https://laas.hal.science/hal-01887434v1/document; https://laas.hal.science/hal-01887434v1/file/212630.pdf |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.F8499ACE |
| Database: |
BASE |