Abstract
In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.
Highlights
Two-Dimensional LocalizationTechnology for estimating the location of workers in indoor environments has been studied for accident prevention and convenience at construction and industrial sites
The multilateration method and the fingerprint method are well known as localization techniques that use radio signals such as Wi-Fi, Zigbee, RFID, and Bluetooth
The received signal strength of radio signals is collected at all points of interest, and the real-time data at a specific position are correlated with the precollected data to estimate its location [13]
Summary
Technology for estimating the location of workers in indoor environments has been studied for accident prevention and convenience at construction and industrial sites. It is known that the range and the localization accuracy of both the multilateration and the fingerprint methods with Wi-Fi, ZigBee, and Bluetooth are inferior to those localization schemes that use radars, which provide high time resolution for estimating distances and locations [11,14,15,16]. The distance estimation tends to be somewhat inaccurate due to random occurrences in indoor environments [15,16] To overcome this limitation, a distance estimation scheme that exploits the deep learning technology of artificial neural networks is introduced to improve the accuracy of distance estimation in [18]. The deep learning technology of artificial neural networks is employed to overcome the limitations of the conventional 2D localization scheme based on multilateration methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
