We propose a drone classification method based on convolutional neural network (CNN)
and micro-Doppler signature (MDS). The MDS only presents Doppler information in time …

Objective: In this paper, we demonstrate the applicability of radar for gait classification with
application to home security, medical diagnosis, rehabilitation, and assisted living. Aiming at …

In recent years, radar has been employed as a fall detector because of its effective sensing
capabilities and penetration through walls. In this paper, we introduce a multilinear …

Deep neural networks (DNNs) have become a relevant subject in the classification of radio
frequency signals and remote sensing data. A primary challenge is a tradeoff between …

Mechanical–structural vibration and/or rotational parts of the targets will induce micro‐
Doppler frequency in addition to the main Doppler components. In the today's military era …

The utilization of radio-frequency (RF) sensing in cyber-physical human systems, such as
human-computer interfaces or smart environments, is an emerging application that requires …

Noncontact penetrating detection and classification of human activities based on micro-
Doppler signatures (MDs) using ultrawideband (UWB) bio-radars are valuable tasks in …

Radar-based assisted living has received significant amount of research interest in recent
years. However, most of the existing works are focused only on free space detection. When it …

Micro-Doppler signature analysis and speech processing share a common approach as
both rely on the extraction of features from the signal's time-frequency distribution for …

Radar-based monitoring of human gait has become of increased interest with applications to
security, sports biomechanics, and assisted living. Radar sensing offers contactless …