CSI-based Vehicle Recognition: Utilizing Convolutional Neural Network Methods in Wireless Sensing

With the development of Intelligent Transportation Systems (ITS), wireless sensing-based vehicle recognition (WsVRE) methods have emerged as a research hotspot in recent years. Compared to traditional vehicle recognition methods, WsVRE methods offer advantages such as strong interference resistance, low equipment costs, and ease of deployment, thereby elevating the intelligence level of ITS. Among the representative wireless sensing technologies, channel state information (CSI) based WsVRE methods have been widely applied in ITSs. However, existing CSI-based WsVRE methods have mainly focused on studying the impact of vehicle recognition algorithms on recognition performance while neglecting the influence of wireless signal attributes on the same. To address this gap and inspired by the recent advances in deep learning techniques, this paper proposes a deep learning based WsVRE method. The study employs deep learning as a tool to investigate the vehicle recognition performance under different antenna deployment positions. Furthermore, to enhance vehicle recognition accuracy, a novel CSI subcarrier filtering method is introduced. Experimental results demonstrate that the WsVRE method performs optimally with wireless signals at an antenna height of 0.6 m, achieving an accuracy of up to 97.14%.