While non-contact monitoring of human respiration has been demonstrated using Doppler radar, the concurrent monitoring of multiple equidistant subjects remains a significant technological challenge. Reported research has so far been limited to maintaining 1-m subject separation, based on the radar antenna beam-width. Proposed here is a hybrid method consisting of an SNR-based intelligent decision algorithm which integrates two different approaches to isolate respiratory signatures of two subjects within the radar beam-width separated by less than 1 meter. Using Independent Component Analysis with the JADE algorithm (ICA-JADE) and Direction of Arrival (DOA), this SNR-based decision algorithm works with an accuracy above 93%. In addition, angular location of each subject is estimated by phase-comparison monopulse and an integrated beam switching capability is demonstrated to optimally extract respiratory information. The proposed method coherently combines two separation methods to overcome multiple-subject monitoring limits which can lead to practical adoption for many respiration monitoring applications.INDEX TERMS Doppler radar, direction of arrival (DOA), joint approximate diagonalization of eigenmatrices (JADE), non-contact measurement, respiration monitoring, subject separation, vital signs.
Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts in authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society.
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