A novel cascaded deep neural network for analyzing smart phone data for indoor localization

2019 ◽  
Vol 101 ◽  
pp. 760-769 ◽  
Author(s):  
Md Rafiul Hassan ◽  
Md Sarwar M. Haque ◽  
Muhammad Imtiaz Hossain ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Smart Phone

scholarly journals DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 133 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Sangjoon Park ◽  
Yongwan Park
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Ensemble Classifier ◽  
Location Based Services ◽  
Magnetic Data ◽  
Neural Network Ensemble ◽  
Localization Accuracy ◽  
Mean Error ◽  
The Impact

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

scholarly journals Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

2018 ◽  
Vol 8 (7) ◽  
pp. 1062 ◽  
Author(s):  
Abebe Adege ◽  
Hsin-Piao Lin ◽  
Getaneh Tarekegn ◽  
Shiann-Shiun Jeng
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Building Environment

scholarly journals LC-DNN: Local Connection Based Deep Neural Network for Indoor Localization With CSI

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108720-108730
Author(s):  
Wen Liu ◽  
Hong Chen ◽  
Zhongliang Deng ◽  
Xinyu Zheng ◽  
Xiao Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Local Connection

scholarly journals Convolutional neural network-based activity monitoring for indoor localization

2021 ◽  
Author(s):  
László Árvai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Indoor Localization ◽  
Smart Phone ◽  
Outdoor Environment ◽  
Smart Devices ◽  
Specific Information ◽  
Motion Data ◽  
Motion Type ◽  
The Stability

AbstractLocation specific services are widely used in outdoor environment and their indoor counterpart is gaining more popularity as well. There is no standardized technology exists for indoor localization, usually smart phone is used as a localization platform and the field strength of an existing radio frequency infrastructure is used as the location specific information. Smart devices are also equipped with several sensors capable of capturing the motion data of the device. Detecting the walking step, turn, stairs motion type can refine the indoor position using digital indoor map as a reference. The real-time recognition of the motion type is possible with a precisely constructed and trained convolutional neural network and therefore it can improve the stability of the localization.

Sign in / Sign up

Export Citation Format

Share Document