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Developments in sensing technology have shown that UWB radars are becoming increasingly valuable sensing devices that can be used for monitoring of humans in military/police and civilian areas. It is known that the applicability of particular methods of human localization depends on the character of persons’ motion. With respect to this finding, the researchers’ attention has been aimed at two fundamental directions. While the former is focused on the localization of moving persons (MP), the latter approach is intended to localize static persons (SP). Then, a proper fusion of the methods developed for MP and SP localization allows monitoring of persons moving with an unknown time-variable character of motion (MP-SP). The analyses of the currently known methods of MP and SP localization in terms of their use for MP-SP localization have shown that while MP localization methods are in principle well developed, SP localization methods are not sufficiently adapted for their use in MP-SP monitoring. Motivated by these findings, we would like to introduce a new radar signal processing procedure for SP localization (SPL) that could be an efficient component of algorithms to be applied for MP-SP monitoring. The novel features of the proposed SPL consist especially in a new approach to SP detection and inclusion of SP tracking in SPL. Moreover, SPL is characterized by relatively low computational complexity and is, therefore, suitable for real-time implementation. Experimental results have shown that SPL introduced in this paper provides very good performance for multiple person localization for line-of-sight and through-the-wall scenarios. [ABSTRACT FROM AUTHOR]

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions. [ABSTRACT FROM AUTHOR]

In this paper, ultra-wideband (UWB) Doppler radar signatures from walking human subjects are processed with the state-space method (SSM) to analyze compressed radar echoes for the first time. A new mathematical model is proposed to detect the human walking strides using UWB radar, while micro-Doppler ( $\mu $ -D) features are extracted for gait analysis using short-time SSM (STSSM). To distinguish $\mu $ -D signatures of different subjects’ body parts, the SSM used for the characterization of radar target signatures and sensor fusion is applied on a sliding short-time window to enhance the resolution of feature extraction from data collected on a dismount. This method of the application of SSM to sliding short-time data is validated with a full-wave electromagnetic (EM) simulation of a walking subject using the Boulic model that represents the human kinematics. An EM scattering model is then utilized to compare the performance of a short-time Fourier transform with STSSM. Experimental results show that STSSM can be successfully applied to identify multiple $\mu $ -D trajectories in real experimental data, thus demonstrating the capability to positively identify human motions (right foot, left foot, and torso) even in a low signal-to-noise ratio environment. [ABSTRACT FROM AUTHOR]