On Using Spatial and Temporal Features for Robust Multiuser Pose Estimation Based on WiFi CSI

Pose estimation based on WiFi channel state information (CSI) has gained popularity in recent years. Most existing works, however, focus only on scenarios with a single user. For those research endeavors on multiuser pose estimation, on the other hand, they suffer from the robustness problem when environment changes since most of them rely on the use of environment-specific features, such as CSI amplitude and phase. In this article, we propose to use both spatial and temporal features for multiuser pose estimation that can show resilience to environment changes. The features we use combine Angle of Arrival (AoA), Time of Flight (ToF), and Doppler frequency shift to capture the location and motion information of the users. Unlike existing methods that extract such features along the time or frequency axis based on a particular packet, we propose three methods to jointly estimate AoA-ToF and AoA-Doppler heatmaps from consecutive packets across multiple subcarriers to increase the resolutions of the desired features. We use mathematical analysis to show the performance benefits of the proposed methods, along with ray-tracing-based simulation and real-life testbed experiments. The simulation shows that the proposed methods can systematically suppress noises and increase the resolutions of desired features compared to existing methods. In addition, the results based on the testbed experiments show that our system outperforms the existing model by 12% in precision when layout of the environment changes, thus substantiating the benefits of using both spatial and temporal features for robust multiuser pose estimation.