Bioradar technology, a novel noncontact detection technology that monitors the vital signs of the human body, has attracted the attention of researchers and scholars in recent years. It has been gradually improved, influenced by developments in a number of fields, including biomedical engineering, radar, computers, and electronics as well as by the growing application requirements of medicine, rescue and military operations, public security, and human-machine interaction. Bioradar technology combines the technologies of radar and biomedical engineering. It can detect tiny physiological movements caused by human vital signs (i.e., breathing and heartbeat) within a certain distance and through nonmetallic substances (such as brick walls, ruins, and rubble) without using electrodes; it is thus considered a powerful tool for a variety of civilian, law enforcement, and military applications.

Recent advances in radar systems have demonstrated the possibility of noncontact human vital signs detection. Most prior research has focused on vital sign detection with specific subjects’ orientations. So far, very few reported studies have been carried out on vital sign detection in any orientation, which is necessary to be applicable in various scenarios, e.g., assisting in the daily life of elderly persons living alone. To tackle this problem, this article presents a scheme that uses a distributed radar network, which transmits stepped frequency continuous-wave (SFCW) signals, then collecting the vital sign data returned from different directions of the subject and subsequently obtaining the respiratory and heartbeat rates from these collected data. A new respiratory and heartbeat model is proposed here for the theoretical analysis and simulation of vital sign detection for any orientation utilizing a distributed radar network. A modified independent component analysis (ICA) method noted as derivative independent component analysis (DICA) is proposed here to estimate the respiratory and heartbeat rates from multiple channels of the distributed radar network. Use of the ICA method was theoretically validated first to prove that the problem formulation satisfies the three conditions required for its implementation. Vital signs of a stationary subject were detected in simulation and measurement using multiple receiving antennas that are fan-shaped distributed around the subject from different directions. In this scenario, both the heartbeat and respiratory rates can be accurately estimated using the proposed DICA algorithm. Moreover, we also experimentally investigated the performance of the DICA method with different combinations of channels as input signals to the DICA algorithm. Both the simulated and experimental results have demonstrated that, by using a distributed radar network and the DICA method, a successful and accurate respiratory and heartbeat rate detection of a subject regardless of subject’s orientation can be achieved due to the independence of their non-Gaussian sources.

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.

The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method.

Non-contact penetrating detection and classification of human activities based on Micro-Doppler signatures (MDs) using UWB bio-radars are valuable tasks in various practical applications such as post-disaster search-and-rescue operations and urban military operations. However, for all classifiers, MD features of different magnitude activities at different positions are likely to result in classification errors due to MD attenuation and confusions. This paper proposes a classifier improving method called position-information-indexed classifier (PIIC). It aims at enhancing the performance of various classifiers in terms of recognition and classification. This method fully exploits the position information acquired by UWB bio-radar to create position-labeled modularized database of MD features. It also guides searching adaptively for optimal predict sub-model of PIICs for activity classification at a random position. We report through-wall detection and classification experimental results related to five activities within a range of 6 m. These results, based on four typical classifiers, demonstrates that PIIC-based classifiers can avoid those classification errors in an effective manner. Moreover, all PIIC-based classifiers presents a better classification performance with an average accuracy rise of 8.16% compared with those of overall-model-based classifiers. These performance evaluation experiments suggest that this method is strongly robust and stable, presenting wide applicability to various classifiers.

Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.