Indoor Localization and Tracking Using 802.11 Networks and Smartphones

2013 ◽  
pp. 117-127 ◽  
Author(s):  
João Quintas ◽  
António Cunha ◽  
Pedro Serra ◽  
André Pereira ◽  
Bruno Marques ◽  
...  
Keyword(s):  
Indoor Localization ◽  
802.11 Networks ◽  
Localization And Tracking

RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

2009 ◽  
Vol 3 (5) ◽  
pp. 860-873 ◽  
Author(s):  
Anindya S. Paul ◽  
Eric A. Wan
Keyword(s):  
Indoor Localization ◽  
Sigma Point ◽  
Localization And Tracking

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

2021 ◽  
pp. 101-107
Author(s):  
Mohammad Alshehri ◽  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Initial Position ◽  
Swarm Optimization ◽  
The Real ◽  
Assisted Navigation ◽  
Glowworm Swarm Optimization ◽  
Tracking Process ◽  
Wide Range ◽  
Localization And Tracking

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

Marker-Free Indoor Localization and Tracking of Multiple Users in Smart Environments Using a Camera-Based Approach

2013 ◽  
pp. 349-357 ◽  
Author(s):  
Andreas Braun ◽  
Tim Dutz ◽  
Michael Alekseew ◽  
Philipp Schillinger ◽  
Alexander Marinc
Keyword(s):  
Indoor Localization ◽  
Smart Environments ◽  
Multiple Users ◽  
Marker Free ◽  
Localization And Tracking

scholarly journals iPAC: Integrate Pedestrian Dead Reckoning and Computer Vision for Indoor Localization and Tracking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 183514-183523 ◽  
Author(s):  
Danyang Li ◽  
Yumeng Lu ◽  
Jingao Xu ◽  
Qiang Ma ◽  
Zhuo Liu
Keyword(s):  
Computer Vision ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Localization And Tracking ◽  
Pedestrian Dead Reckoning

scholarly journals TOA-Based Indoor Localization and Tracking With Inaccurate Floor Plan Map via MRMSC-PHD Filter

2019 ◽  
Vol 19 (21) ◽  
pp. 9869-9882 ◽  
Author(s):  
Heng Zhang ◽  
Soon Yim Tan ◽  
Chee Kiat Seow
Keyword(s):  
Indoor Localization ◽  
Floor Plan ◽  
Localization And Tracking

scholarly journals Scanning method for indoor localization using the RSSI approach

2017 ◽  
Vol 6 (1) ◽  
pp. 247-251 ◽  
Author(s):  
Ahmad Warda ◽  
Bojana Petković ◽  
Hannes Toepfer
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Reconstruction Error ◽  
Received Signal Strength Indicator ◽  
Contrast Resolution ◽  
Scanning Method ◽  
Database Construction ◽  
Indoor Mobile Robot ◽  
Mobile Robot Localization ◽  
Localization And Tracking

Abstract. This paper presents a scanning method for indoor mobile robot localization using the received signal strength indicator (RSSI) approach. The method eliminates the main drawback of the conventional fingerprint, whose database construction is time-consuming and which needs to be rebuilt every time a change in indoor environment occurs. It directly compares the column vectors of a kernel matrix and signal strength vector using the Euclidean distance as a metric. The highest resolution available in localization using a fingerprint is restricted by a resolution of a set of measurements performed prior to localization. In contrast, resolution using the scanning method can be easily changed using a denser grid of potential sources. Although slightly slower than the trilateration method, the scanning method outperforms it in terms of accuracy, and yields a reconstruction error of only 0. 08 m averaged over 1600 considered source points in a room with dimensions 9.7 m × 4.7 m × 3 m. Its localization time of 0. 39 s makes this method suitable for real-time localization and tracking.

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