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We investigated the feasibility of using Doppler radar to recognize human voices by capturing the micro-Doppler signatures of vibrations from the larynx and mouth. The signatures produced through the vibrations of a human being’s vocal cords generate unique micro-Doppler signatures, depending on the letters pronounced. These can then be used to classify and recognize different words and letters. In this paper, we could successfully capture echo signals using the Doppler radar when a human subject spoke seven musical notes from Do to Ti and alphabet letters from $\boldsymbol {A}$ to $\boldsymbol {Z}$. Spectrogram analysis was conducted for classification purposes, and the deep convolutional neural networks employed could classify the 26 letters to an accuracy of 94%. To overcome the deficiency of the measured data and improve the classification accuracy, transfer learning was introduced. Using the VGG-16 model, its accuracy was improved up to 97%. Additional experiments were conducted to ascertain the radar’s capability to detect the human voice through a barrier between the human and the radar. In this paper, we demonstrated the possibility of remote voice recognition using Doppler information, with or without a barrier. [ABSTRACT FROM AUTHOR]

The demand for detecting and tracking drones has increased for reasons of surveillance and security. Radar is one of the promising methods in this regard. The recognition and identification of drones using a radar system requires the extraction of their unique micro‐Doppler signatures produced by their rotating blades. Because of the blades' rapid rotation speed, difficulties are inherent in visualizing clear micro‐Doppler signatures in a conventional joint time‐frequency analysis such as the short‐time Fourier transform. In this paper, we propose the use of high‐resolution transform techniques to visualize the micro‐Doppler signatures of drones in a spectrogram. The techniques used include Wigner‐Ville distribution, smoothed pseudo‐Wigner‐Ville distribution, and short‐time MUltiple SIgnal Classification (MUSIC) algorithm. In particular, the latter, which had never previously been applied to drones, is suggested to visualize the details of micro‐Doppler signatures. We measured three drones using a continuous‐wave radar, and performances of these algorithms were compared using data collected from the drones. We could observe that the short‐time MUSIC method showed the clearest spectrogram for identifying micro‐Doppler signatures. This study can potentially be useful in the field of drone classification. [ABSTRACT FROM AUTHOR]

This article deals with the development of a dual channel S-Band frequency-modulated continuous wave (FMCW) system for a through-the-wall imaging (TWRI) system. Most existing TWRI systems using FMCW were developed for synthetic aperture radar (SAR) which has many drawbacks such as the need for several antenna elements and movement of the system. Our implemented TWRI system comprises a transmitting antenna and two receiving antennas, resulting in a significant reduction of the number of antenna elements. Moreover, a proposed algorithm for range-angle-Doppler 3D estimation based on a 3D shift invariant structure is utilized in our implemented dual channel S-band FMCW TWRI system. Indoor and outdoor experiments were conducted to image the scene beyond a wall for water targets and person targets, respectively. The experimental results demonstrate that high-quality imaging can be achieved under both experimental scenarios. [ABSTRACT FROM AUTHOR]