论文标题
使用经常性神经网络的通道状态信息，厘米级室内定位
Centimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks
论文作者
论文摘要
物联网或自动驾驶中的现代技术需要更准确的定位。经典位置技术主要适应室外场景，而它们不符合具有多个路径的室内案例的要求。同时，作为噪声和时间变化的功能，通道状态信息（CSI）表明，其优于接收的信号强度指示器（RSSI）的优势更准确。为此，本文提出了神经网络方法，以通过从线性天线收集的实际CSI数据来估计厘米级室内定位。它利用通道响应的振幅或相关矩阵作为输入，这可以高度降低数据大小并抑制噪声。此外，它利用了通过复发神经网络（RNN）和信号 - 噪声比（SNR）信息的用户运动轨迹的一致性，这可以进一步提高估计准确性，尤其是在小型数据研究中。这些贡献都根据其他经典监督学习方法的结果，使神经网络的效率受益。
Modern techniques in the Internet of Things or autonomous driving require more accuracy positioning ever. Classic location techniques mainly adapt to outdoor scenarios, while they do not meet the requirement of indoor cases with multiple paths. Meanwhile as a feature robust to noise and time variations, Channel State Information (CSI) has shown its advantages over Received Signal Strength Indicator (RSSI) at more accurate positioning. To this end, this paper proposes the neural network method to estimate the centimeter-level indoor positioning with real CSI data collected from linear antennas. It utilizes an amplitude of channel response or a correlation matrix as the input, which can highly reduce the data size and suppress the noise. Also, it makes use of the consistency in the user motion trajectory via Recurrent Neural Network (RNN) and signal-noise ratio (SNR) information, which can further improve the estimation accuracy, especially in small datasize learning. These contributions all benefit the efficiency of the neural network, based on the results with other classic supervised learning methods.
