Optimized Wireless Sensing and Deep Learning for Enhanced Human-Vehicle Recognition

In the realm of traffic parameter measurement, wireless sensing-based human-vehicle recognition methods have been pivotal due to their low cost and non-invasive nature. Traditionally, these methods have relied on the 2.4 GHz frequency band, often neglecting the rich potential of the sub-GHz bands. Furthermore, the energy attributes of wireless signals are influenced by both antenna height and carrier frequency, yet few studies have explored their impact on human-vehicle recognition performance. Addressing this critical research gap, this study introduces an innovative convolutional neural network-based method that leverages both sub-GHz bands and variable antenna heights. Specifically, this paper focuses on two key aspects: received signal strength signal-to-noise ratio analysis and wireless sensing-based human-vehicle recognition performance analysis. Experimental results demonstrate that the optimal human-vehicle recognition performance is achieved with 2.4 GHz wireless signals and an antenna height of 0.8 m, resulting in an average vehicle recognition accuracy of 95.96%. Besides, the dataset with various carrier frequencies and antenna heights has been publicly available at
https://github.com/TZ-mx/mixed-RSS-dataset
.