In recent years, wi-fi has become the main gateway that connects users to the Internet. Considering the availability of wi-fi signals, and their suitability for channel estimation, IEEE established the wi-fi Sensing (...
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In recent years, wi-fi has become the main gateway that connects users to the Internet. Considering the availability of wi-fi signals, and their suitability for channel estimation, IEEE established the wi-fi Sensing (WS) Task Group whose purpose is to study the feasibility of wi-fi-based environment sensing. However, wi-fi signals are transmitted over limited bandwidths with a relatively small number of antennas in bursts, fundamentally limiting the range, Angle-of-Arrival and speed resolutions. This paper presents a super-resolutionalgorithm to perform the parameter estimation in a quasi-monostatic WS scenario. The proposed algorithm, RIVES, estimates the range, Angle-of-Arrival and speed parameters with Vandermonde decomposition of Hankel matrices. To estimate the size of the signal subspace, RIVES uses a novel Model Order Selection method which eliminates spurious noise targets based on their distance to the noise and signal subspaces. Various scenarios with multiple targets are simulated to show the robustness of RIVES. In order to prove its accuracy, real-life indoor experiments are conducted with human targets by using Software Defined Radios.
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