The ubiquitous wireless network infrastructure and the need of people’s indoor sensing inspire the work leveraging wireless signal into broad spectrum for indoor applications, including indoor localization, human–computer interaction, and activity recognition. To provide an accurate model selection or feature template, these applications take the system reliability of the signal in line-of-sight and non-line-of-sight propagation into account. Unfortunately, these two types of signal propagation are analyzed in static or mobile scenario separately. Our question is how to use the wireless signal to estimate the signal propagation ambience to facilitate the adaptive complex environment? In this paper, we exploit the Fresnel zone theory and channel state information (CSI) to model the static and mobile ambience detectors. Considering the spatiotemporal correlation of indoor activities, the propagation ambience can be divided into three categories: line-of-sight (LOS), non-line-of-sight (NLOS), and semi-line-of-sight (SLOS), which is used to represent the intermediate state between the LOS and NLOS propagation ambience during user movement. Leveraging the hidden Markov model to estimate the dynamic propagation ambience in the mobile environment, a novel propagation ambience identification method, named Ambience Sensor (Asor), is proposed to improve the real-time performance for the upper applications. Furthermore, Asor is integrated into a localization algorithm, Asor-based localization system (Aloc), to confirm the effectiveness. We prototype Asor and Aloc based on commodity WiFi infrastructure without any hardware modification. In addition, the real-time performance of Asor is evaluated by conducting tracking experiments. The experimental results show that the median detection rate of propagation ambience is superior to the existing methods in absence of any a priori hypothesis of static or mobile scenarios.
Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging
