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Radar has been widely applied in many scenarios as a critical remote sensing tool for non-contact vital sign monitoring, particularly for sleep monitoring and heart rate measurement within the home environment. For non-contact monitoring with radar, interference from house pets is an important issue that has been neglected in the past. Many animals have respiratory frequencies similar to those of humans, and they are easily mistaken for human targets in non-contact monitoring, which would trigger a false alarm because of incorrect physiological parameters from the animal. In this study, humans and common pets in families, such as dogs, cats, and rabbits, were detected using an impulse radio ultrawideband (IR-UWB) radar, and the echo signals were analyzed in the time and frequency domains. Subsequently, based on the distinct in-body structure between humans and animals, we propose a parameter, the respiratory and heartbeat energy ratio (RHER), which reflects the contribution rate of breathing and heartbeat in the detected vital signs. Combining this parameter with the energy index, we developed a novel scheme to distinguish between humans and animals. In the developed scheme, after background noise removal and direct-current component suppression, an energy indicator is used to initially identify the target. The signal is then decomposed using a variational mode decomposition algorithm, and the variational intrinsic mode functions that represent human respiration and heartbeat components are obtained and utilized to calculate the RHER parameter. Finally, the RHER index is applied to rapidly distinguish between humans and animals. Our experimental results demonstrate that the proposed approach more effectively distinguishes between humans and animals in terms of monitoring vital signs than the existing methods. Furthermore, its rapidity and need for only minimal calculation resources enable it to meet the needs of real-time monitoring.

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls., Competing Interests: The authors declare no conflict of interest.

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., Competing Interests: The authors declare no conflict of interest.

It is a major challenge to search for survivors after chemical or nuclear leakage or explosions. At present, biological radar can be used to achieve this goal by detecting the survivor's respiration signal. However, owing to the random posture of an injured person at a rescue site, the radar wave may directly irradiate the person's head or feet, in which it is difficult to detect the respiration signal. This paper describes a multichannel-based antenna array technology, which forms an omnidirectional detection system via 24-GHz Doppler biological radar, to address the random positioning relative to the antenna of an object to be detected. Furthermore, since the survivors often have random body movement such as struggling and twitching, the slight movements of the body caused by breathing are obscured by these movements. Therefore, a method is proposed to identify random human-body movement by utilizing multichannel information to calculate the background variance of the environment in combination with a constant-false-alarm-rate detector. The conducted outdoor experiments indicate that the system can realize the omnidirectional detection of random human-body movement and distinguish body movement from environmental interference such as movement of leaves and grass. The methods proposed in this paper will be a promising way to search for survivors outdoors.

Different speech detection sensors have been developed over the years but they are limited by the loss of high frequency speech energy, and have restricted non-contact detection due to the lack of penetrability. This paper proposes a novel millimeter microwave radar sensor to detect speech signals. The utilization of a high operating frequency and a superheterodyne receiver contributes to the high sensitivity of the radar sensor for small sound vibrations. In addition, the penetrability of microwaves allows the novel sensor to detect speech signals through nonmetal barriers. Results show that the novel sensor can detect high frequency speech energies and that the speech quality is comparable to traditional microphone speech. Moreover, the novel sensor can detect speech signals through a nonmetal material of a certain thickness between the sensor and the subject. Thus, the novel speech sensor expands traditional speech detection techniques and provides an exciting alternative for broader application prospects.

To determine whether a living object is human being or animal by detecting the respiration signal using the life-detector based on radar to penetrate the building or debris. To fully utilize the nonstationary character of the respiration signal, short time Fourier transform (STFT) is employed to get the signal's time-frequency representation. Singular value decomposition (SVD) is then used in the spectrogram to extract feature vector for pattern identification. Human beings and animals can be identified from the respiration sensed by the life-detector based on radar. Experimental results show that the method based on STFT and SVD is stable and efficient to differ from the human being and animal.

In this article, a channel micro-Doppler ($\mu \text{D}$) signatures’ quantitative evaluation method for multiple-input–multiple-output (MIMO) radar system, named relative signal–noise ratio in the time–frequency domain and denoted as RSNRtf, is introduced. The impact on RSNRtf by some key dominant factors in real practical and diverse detection environments was theoretically analyzed first. Then, this proposed method is exploited to conduct effective and superior channel selection for improved human activity classification at random human target positions. Based on numerous MIMO stepped-frequency continuous wave (SFCW) radar data from eight subjects with seven activities at nine positions in a through-wall within a view angle of ±20° and up to 6-m distance away, the influence of various primary factors on the proposed method is investigated. Investigative experiments showed that the proposed method has a wide range of applicability and a high interference tolerance even for different detection distances, angles, activities, and subjects. Classification experimental results based on typical classifiers also showed that the proposed method could effectively improve human activity classification. Moreover, we can claim that the proposed method can achieve a higher outstanding performance in the channel $\mu \text{D}$ capacity evaluation and enhanced classification for the MIMO-radar-based human activity classification in free space or with a wider detection field with large angle range. [ABSTRACT FROM AUTHOR]

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 comes to the through-the-wall human motion detection, the wall media and indoor static non-human targets would cause clutters and significantly corrupt the motion information of human targets behind wall. In this work, we first propose to use a low center-frequency ultra-wideband (UWB) radar system to probe the behind wall scene. Then, a Robust Principal Component Analysis (RPCA) based subspace decomposition technique is employed not only to remove the stationary clutters in raw range slow-time map but also to mitigate the multipath effects in the time-frequency map. Onsite experiments of human motions detection behind a single layer of reinforced concrete wall are carried out to investigate the performance of the technique. Lastly, a two-dimensional (2D) PCA based method and a convolutional neural network (CNN) based method are employed for the motion classification. The results based on the enhanced features obtained by the proposed method show a higher accuracy than those based on the traditional features through mean value subtraction. [ABSTRACT FROM AUTHOR]

In through-the-wall human imaging, ambiguities in wall parameters will cause image blurred and deteriorate signal-to-clutter ratio of vital signs. Autofocusing method is a kind of method to get focusing image without any information of wall. This method is based on the idea that the focusing performance improves when estimated wall parameters are close to the true parameters. In traditional autofocusing methods, the interesting targets such as the point-like targets are selected as the reference targets to assess the focusing performance of image. However, in some cases, especially in through-the-wall imaging of human vital signs, the existence of human cannot be assured before detection and discrimination. Nevertheless, high-performance detection of human is the aim of imaging. The wall always exists in the imaging area, and reflections of wall edges are stronger than the interested vital signs. Thus, in this study the authors select the edges of wall as the prominent targets. The linearity degree of wall edges is adopted as the assessment item of focusing performance since the images of wall edges after compensating the wall parameters are lines approximately. Simulation results show that the proposed autofocusing method can effectively improve the focusing performance of human vital signs. [ABSTRACT FROM AUTHOR]

Efficient detection and monitoring of physiological conditions and activity hidden by walls or rubble are considered challenging in rescue, surveillance, and security operations. In this paper, a new method based on wavelet analysis called wavelet entropy (WE) is proposed to not only detect vital signs but also to monitor the target’s physiological conditions through ultra-wideband (UWB) radar with a centre frequency of 400 MHz. Results of the experiments show that this new method can accurately locate a human target in free space, through a brick wall at a certain distance, and in cases of simulated rubble. Furthermore, a human target’s micro-motion responses due to different physiological conditions with a stationary body in the case of simulated rubble, and that are unrecognizable using common methods, were also quantitatively detected. The WE method not only locates the target’s position but also monitors changes in his/her physiological conditions. This method may also be a useful tool in the practical application of UWB radar. [ABSTRACT FROM PUBLISHER]

Simple Summary: In this paper, we presented a new non-contact method to measure the vital signs of at-rest pets, where the ultra-wideband radar was applied for previously unachievable sensing ability and testing convenience. The proposed scheme did not interfere with the daily rhythm of the pet being tested; most subjects would not even notice the ongoing real-time monitoring due to the larger measurement coverage of the radar sensor. Through this work, we provided an innovative tool to promote not only the measurement of pet vital signs but also the improvement of animal welfare to prevent invasive, dangerous, and unacceptable contact measurement techniques, such as anesthesia, hair removal, and surgical implants. In addition, the proposed method could realize the daily vital signs measurement and sleep monitoring of dogs and cats, which is important for the diagnosis and timely treatment of pet diseases. As pets are considered members of the family, their health has received widespread attention. Since pets cannot talk and complain when they feel uncomfortable, monitoring vital signs becomes very helpful in disease detection, as well as observing their progression and response to treatment. In this study, we proposed an ultra-wideband radar-based, non-contact animal vital sign monitoring scheme that could monitor the breathing and heart rate of a pet in real-time. The primary advantage of the ultra-wideband radar was its ability to operate remotely without electrodes or wires and through any clothing or fur. Because of the existing noise and clutter in non-contact detection, background noise removal was applied. Furthermore, the respiration rate was directly obtained through spectrum analysis, while the heartbeat signal was extracted by the variational mode decomposition algorithm. By using electrocardiogram measurements, we verified the accuracy of the radar technology in detecting the anesthetized animals' respiratory rate and heart rate. Besides, three beagles and five cats in a non-sedated state were measured by radar and contact pressure sensors simultaneously; the experimental results showed that radar could effectively measure the respiration of cats and dogs, and the accuracy rate was over 95%. Due to its excellent performance, the proposed method has the potential to become a new choice in application scenarios, such as pet sleep monitoring and health assessment. [ABSTRACT FROM AUTHOR]

Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare. [ABSTRACT FROM AUTHOR]

Radar-based life imaging technology has been widely applied in both civilian and military applications. Wall penetration attenuation degrades the signal-to-clutter-and-noise ratio (SCNR) and radar portability restricts the spatial resolution of the high-dimensional image. In this study, we constructed a portable linear-frequency continuous-wave (LFMCW) multiple-input multiple-output (MIMO) radar system, and we presented a novel high-dimensional imaging framework. We combined the maximum spectrum peak, fourth-order cumulant (FOC) and 3D coherent factor (CF) to enhance vital signs based on attention mechanism. We improve resolution by deconvolution algorithm using the data from a human dummy. Visualization-enhanced 4D point clouds images were created by mapping micro-doppler-frequency to color. We employed a series of experiments to validate the proposed method, including human and beagle imaging in free space, and short-range and long-range (16 m) human imaging through a wall. These high-dimensional imaging results validated the portable radar system's potential in detection, identification, interpretation of human targets. [ABSTRACT FROM AUTHOR]

[Display omitted] Radar-based life detecting technology has been researched more and more in recent years and applied widely in several areas. However, few researches on multi-subjects detecting and locating have been reported. Even these researches are mainly based on imaging technology with two shortcomings, namely the interference of ghost and difficult to focus the target's position. A new coarse-to-fine approach for detecting and locating of human targets using single-input multiple-output (SIMO) radar under penetration conditions is proposed. The approach realized automatic recognition and localization of human subjects without any prior information, such as target count. Firstly, an improved censored mean level detector constant false alarm rate (CMLD-CFAR) by adding a feature to distinguish vital sign from background is proposed in each inner-channel to coarsely determine the targets' time-of-arrival (TOA) distances. Secondly, the real targets count is finely determined by calculating the inter-channel Pearson correlation coefficient of signals from different channels. Then, a back projection (BP) is presented to calculate the targets' 2-D locations. In the experiments of single and multi-target locating, all targets were identified correctly and localized near their actual positions with errors within 0.3 m. With the distinctive telescopic structure of the proposed radar, the azimuth resolution of locating is adjustable. Compare to current radar system for radar multi-target locating, like multiple-input multiple-output (MIMO) system, the proposed system can improve accuracy and automation of localization while decline the cost and complexity. It has potential to promote practical application of locating trapped human targets in post-disaster rescue and intelligent homeware fields. [ABSTRACT FROM AUTHOR]