passiveradars 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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passiveradars 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 Model Order Selection (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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