Abstract
In device-free radio frequency (RF) body occupancy inference systems, RF signals encode information (e.g., body location, posture, activity) about moving targets (not instrumented) that alter the radio propagation in the surroundings of the RF link(s). Such systems are now getting more attention as they enable flexible location-based services for new smart scenarios (e.g., smart spaces, safety and security, assisted living) just using off-the-shelf wireless devices. The goal of this paper is to set the fundamental signal processing methods and tools for performance evaluation of passive occupancy inference problems that leverage on the analysis of physical layer (PHY) channel state information (CSI) obtained from multiple antennas (spatial domain) and carriers (frequency domain) jointly. To this aim, we consider here a multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) radio interface adopted in high-throughput WiFi networks such as IEEE 802.11n,ac. The proposed approach investigates at first relevant CSI features that are more sensitive to body presence; next, it proposes a space-frequency selection method based on principal component analysis (PCA). Considering an experimental case study with WiFi links, we show that the joint space- and frequency-domain processing of the radio signal quality indicators enable both detection and localization of two independent targets (i.e., human bodies) arbitrarily moving in the surroundings of the transmitter/receiver locations. Experiments are conducted using off-the-shelf WiFi devices configured to extract and process CSI over standard PHY preambles: performance analysis sets the best practices for system design and evaluation.
Highlights
Device-free body occupancy inference and localization/tracking systems are generally designed to passively evaluate the position, size, and orientation of human bodies or objects placed near a radio link without the need to instrument the monitored targets [1,2,3]
7 Conclusions This paper proposed signal processing methods and tools to enable passive body occupancy detection and localization based on the analysis of the channel state information (CSI) collected from multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) radio interfaces (i.e., WiFi devices)
physical layer (PHY) layer CSI data is processed over both space and frequency domains to isolate relevant perturbations induced by target movements
Summary
Device-free body occupancy inference and localization/tracking systems are generally designed to passively evaluate the position, size, and orientation of human bodies or objects (i.e., the targets) placed near a radio link without the need to instrument the monitored targets [1,2,3]. Radio frequency (RF) signals commonly adopted for wireless communications are perturbed by the presence of targets, their movements, and the changing surrounding scenario. This happens due to the propagation of the electromagnetic (EM) waves and their interactions with the target(s) and the environment through reflection, scattering, and diffraction phenomena. Kianoush et al EURASIP Journal on Advances in Signal Processing (2018) 2018:44 all wireless devices, RSS has been adopted for many inference problems such as localization [10,11,12], activity recognition [13, 14], target size evaluation [15], and detection [16]. The use of physical layer (PHY) raw signal features, not yet fully standardized [10], is believed to provide significant performance improvements
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