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Cited by 281 publications
(117 citation statements)
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“…Pilot leverages the correlation of CSI over time to monitor abnormal appearance and further locate the entity. PADS [3] and DeMan [4] further exploit full information (both amplitude and phase) of CSI, extracting the maximum eigenvalues of the covariance matrix from successive CSI to enhance detection performance. OmniPHD [24] achieves the omnidirectional sensing coverage for passive human detection in typical multipath-rich indoor scenarios.…”
Section: Related Workmentioning
confidence: 99%
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“…Pilot leverages the correlation of CSI over time to monitor abnormal appearance and further locate the entity. PADS [3] and DeMan [4] further exploit full information (both amplitude and phase) of CSI, extracting the maximum eigenvalues of the covariance matrix from successive CSI to enhance detection performance. OmniPHD [24] achieves the omnidirectional sensing coverage for passive human detection in typical multipath-rich indoor scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…In the previous work, various metrics such as variance, mean, and distribution distance have been exploited for detection. Previous work [3,4] only seeks for amplitude-based or combination of amplitude and phase. In this paper, we leverage phase difference-based features for motion detection.…”
Section: Feature Extractionmentioning
confidence: 99%
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“…Now existing human detection using wireless signal can only detect the moving people. But DeMan utilizes the chest motion caused by heart beat to detect the stationary person ingeniously with the premise determining parameters in the model by doing a small number of prior measurements. Leveraging both the amplitude and phase information of CSI, DeMan also has the ability of detecting moving objects.…”
Section: Human Detectionmentioning
confidence: 99%
“…Wu et al [21] tried to develop methods to detect indoor human precedence with the usage of Wi-Fi signal only. They showed that channel state information (CSI) can be in some cases stable enough to develop a method that can detect stationary and moving people inside the rooms.…”
Section: Introductionmentioning
confidence: 99%