Seong Ju-Hyeon, Lee Soo-Hwan, Kim Won-Yeol, Seo Dong-Hoan
Department of Liberal Education, Korea Maritime and Ocean University, Busan 49112, Korea.
Department of Electrical and Electronics Engineering, Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, Busan 49112, Korea.
Sensors (Basel). 2021 May 26;21(11):3701. doi: 10.3390/s21113701.
Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.
Wi-Fi往返时间（RTT）被应用于基于距离估计的室内定位系统。在存在许多障碍物的非视距（NLOS）环境中，RTT的接收不稳定性高于基于接收信号强度指示（RSSI）的指纹识别，由于多径衰落会导致较大的定位误差。为了解决这些问题，在本文中，我们提出了一种基于高精度RTT的室内定位系统，该系统使用RTT补偿距离网络（RCDN）和区域建议网络（RPN）。所提出的方法包括一个基于卷积神经网络（CNN）的RCDN，用于提高接收距离的预测精度和学习率；以及一个基于循环神经网络的RPN，用于实时定位，以端到端的方式实现。所提出的RCDN通过应用扫描步骤来提高接收不稳定的基于飞行时间（TOF）的RTT的接收率，从每个RTT发射器收集并校正稳定可靠的距离预测值。此外，通过RPN使用基于指纹的位置确定方法来推导用户位置，其中利用快速采样周期的特性对RCDN中校正后的RTT距离进行划分处理。
