Résumé : In recent years, Wi-Fi has become the main gateway that connects users to the internet. Considering the availability ofWi-Fi signals, and their suitability for channel estimation, IEEE established the Wi-Fi Sensing (WS) Task Group whose purposeis to study the feasibility of Wi-Fi-based environment sensing. However, Wi-Fi signals are transmitted over limited bandwidthswith a relatively small number of antennas in bursts, fundamentally limiting the range, Angle-of-Arrival and speed resolutions.This paper presents a super-resolution algorithm to perform the parameter estimation in a quasi-monostatic WS scenario. Theproposed algorithm, RIVES, estimates the range, Angle-of-Arrival and speed parameters with Vandermonde decomposition ofHankel matrices. To estimate the size of the signal subspace, RIVES uses a novel Model Order Selection method which eliminatesspurious noise targets based on their distance to the noise and signal subspaces. Various scenarios with multiple targets aresimulated to show the robustness of RIVES. In order to prove its accuracy, real-life indoor experiments are conducted with humantargets by using Software Defined Radios.