The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance.

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

Ultra-wide band (UWB) radar for short-range human target detection is widely used to find and locate survivors in some rescue missions after a disaster. The results of the application of bistatic UWB radar for detecting multi-stationary human targets have shown that human targets close to the radar antennas are very often visible, while those farther from radar antennas are detected with less reliability. In this paper, on account of the significant difference of frequency content between the echo signal of the human target and that of noise in the shadowing region, an algorithm based on wavelet entropy is proposed to detect multiple targets. Our findings indicate that the entropy value of human targets was much lower than that of noise. Compared with the method of adaptive filtering and the energy spectrum, wavelet entropy can accurately detect the person farther from the radar antennas, and it can be employed as a useful tool in detecting multiple targets by bistatic UWB radar.