Improved indoor localization system based on virtual access points in a Wi-Fi environment by filtering schemes

2017 ◽  
Author(s):  
Boney Labinghisa ◽  
Gu Sam Park ◽  
Dong Myung Lee
Keyword(s):  
Indoor Localization ◽  
Localization System ◽  
Access Points

scholarly journals Smartphone-Based Indoor Localization within a 13th Century Historic Building

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4095 ◽  
Author(s):  
Toni Fetzer ◽  
Frank Ebner ◽  
Markus Bullmann ◽  
Frank Deinzer ◽  
Marcin Grzegorzek
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Weighted Average ◽  
Setup Time ◽  
13Th Century ◽  
Test Scenario ◽  
Optimization Scheme ◽  
Historic Building ◽  
Localization System ◽  
Access Points

Within this work we present an updated version of our indoor localization system for smartphones. The pedestrian’s position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. Our recently presented approximation scheme of the kernel density estimation allows to find an exact estimation of the current position, compared to classical methods like weighted-average. Absolute positioning information is given by a comparison between recent measurements of nearby access points and signal strength predictions. Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a set of reference measurements to estimate a corresponding model. This work provides three major contributions to the system. The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building’s walkable areas. The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes. Within the scope of this work, we tackle problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures. For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter. The goal of this work is to propose a fast to deploy localization solution, that provides reasonable results in a high variety of situations. To stress our system, we have chosen a very challenging test scenario. All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. The system is evaluated using 28 distinct measurement series on four different test walks, up to 310 m length and 10 min duration. It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. The introduced filtering methods allow for a real fail-safe system, while the optimization scheme enables an on-site setup-time of less then 120 min for the building’s 2500 m walkable area.

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

Acoustic indoor-localization system for smart phones

2014 ◽  
Author(s):  
Fabian Hoflinger ◽  
Joachim Hoppe ◽  
Rui Zhang ◽  
Alexander Ens ◽  
Leonhard Reindl ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Smart Phones ◽  
Localization System
Sign in / Sign up

Export Citation Format

Share Document