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Article

A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN

1
Beijing University of Posts and Telecommunications, Beijing 100088, China
2
China Academy of Information and Communication Technology (CAICT), Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(4), 704; https://doi.org/10.3390/s17040704
Received: 27 December 2016 / Revised: 22 March 2017 / Accepted: 23 March 2017 / Published: 28 March 2017
(This article belongs to the Special Issue Smartphone-based Pedestrian Localization and Navigation)
Considering the installation cost and coverage, the received signal strength indicator (RSSI)-based indoor positioning system is widely used across the world. However, the indoor positioning performance, due to the interference of wireless signals that are caused by the complex indoor environment that includes a crowded population, cannot achieve the demands of indoor location-based services. In this paper, we focus on increasing the signal strength estimation accuracy considering the population density, which is different to the other RSSI-based indoor positioning methods. Therefore, we propose a new wireless signal compensation model considering the population density, distance, and frequency. First of all, the number of individuals in an indoor crowded scenario can be calculated by our convolutional neural network (CNN)-based human detection approach. Then, the relationship between the population density and the signal attenuation is described in our model. Finally, we use the trilateral positioning principle to realize the pedestrian location. According to the simulation and tests in the crowded scenarios, the proposed model increases the accuracy of the signal strength estimation by 1.53 times compared to that without considering the human body. Therefore, the localization accuracy is less than 1.37 m, which indicates that our algorithm can improve the indoor positioning performance and is superior to other RSSI models. View Full-Text
Keywords: indoor positioning; smartphone camera; indoor crowded scenarios; population density; RSSI; deep CNN indoor positioning; smartphone camera; indoor crowded scenarios; population density; RSSI; deep CNN
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MDPI and ACS Style

Jiao, J.; Li, F.; Deng, Z.; Ma, W. A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN. Sensors 2017, 17, 704. https://doi.org/10.3390/s17040704

AMA Style

Jiao J, Li F, Deng Z, Ma W. A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN. Sensors. 2017; 17(4):704. https://doi.org/10.3390/s17040704

Chicago/Turabian Style

Jiao, Jichao, Fei Li, Zhongliang Deng, and Wenjing Ma. 2017. "A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN" Sensors 17, no. 4: 704. https://doi.org/10.3390/s17040704

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