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-resolution algorithm 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.
Passive Radars based on Wi-Fi signals provide an excellent opportunity for human sensing without violating the privacy of individuals. Due to the limited integration time of Wi-Fi bursts and relatively low bandwidths,...
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Passive Radars based on Wi-Fi signals provide an excellent opportunity for human sensing without violating the privacy of individuals. Due to the limited integration time of Wi-Fi bursts and relatively low bandwidths, Fourier Transform-based methods do not provide the required accuracy. Herein, a Wi-Fi-based passive radar algorithm is proposed for indoor human movement detection with super resolution which relies on the ESPRIT algorithm to estimate range/speed parameters from limited number of measurements. To determine the number of targets in the environment, a new modelorderselection (MOS) method is proposed which exploits the orthogonality between the basis vectors of signal and noise subspaces obtained from the sample covariance matrix of the measurements. The new MOS method along with the proposed algorithm are numerically analysed and compared with other existing methods. Finally, the performance of the algorithm is experimentally validated in indoor conditions.
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