论文标题
在多径环境中对基于机器学习的范围的初步研究
A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment
论文作者
论文摘要
在多路径环境（例如城市区域）中，无人接地车辆（例如UGV）需要全球导航卫星系统（GNSS）的替代导航技术。在城市地区，长期进化（LTE）信号可以在没有任何额外基础设施的情况下在高功率上无处不在。我们提出了一种基于LTE通道脉冲响应（CIR）的机器学习方法，以估算LTE基站和UGV之间的范围。使用软件定义的无线电（SDR）从LTE物理层中提取了CIR，其中包括来自通道的信号衰减信息。我们设计了一个卷积神经网络（CNN），该神经网络估计以CIR为输入。在我们的现场测试中，所提出的方法比接收的信号强度指标（RSSI）方法表现出更好的范围性能。
Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.
