LAUSR

With the widespread of commercial communication equipment, WiFi signals are ubiquitous in human life. Therefore, utilizing WiFi signals to implement intelligent sensing applications is an inevitable trend. In WiFi sensing applications, through-the-wall crowd counting is a challenging problem. In the through-the-wall scenario, the wireless signal transmitted through the wall will carry a lot of noises and is severely attenuated. Therefore, the influence of human activities on the wireless signal is difficult to extract. To solve this problem, we propose TWCC, a through-the-wall crowd counting system using ambient WiFi signals. TWCC utilizes commercial WiFi equipments to extract the phase difference data of the channel state information (CSI) and transform it to sense the environment. First, TWCC preprocesses the data to remove uncorrelated noise, and then combines the sub-carrier correlation to achieve through-the-wall human detection. When people exist, TWCC extracts features from four domains as feature groups, namely time domain, subcarrier domain, frequency domain, and time-frequency domain. Then TWCC uses different backpropagation (BP) neural networks for the features of the four domains and combines with weighting and threshold judgment to realize the through-the-wall crowd counting detection. Extensive real-world experiments show that TWCC achieves an average recognition accuracy of about 90% and maintains strong robustness to different speeds and environments.