Using radar to contactlessly monitor physiological information plays an important role in advancing the Internet of Health Things (IoHT) industry. In radar-based physiological monitoring tasks, one key issue is how to sense the chest wall micromotions induced by human respiratory and cardiac activities. Unfortunately, these micromotions, which carry physiological signs, are tiny and hard to capture rapidly with high accuracy, making it challenging to balance speed and accuracy—even with the state-of-the-art (SOTA) linear demodulation (LiDe) approach (most accurate but time-consuming). However, in this paper, we explore and develop an improved LiDe method that uses a simple transform and eliminates the need for the singular value decomposition (SVD) used in the original approach, thereby achieving rapid radar sensing of physiological micromotions with SOTA accuracy. This work begins with the surprising discovery that the second component rather than the principal component is valid in LiDe results, and the reasons behind this discovery are also revealed. Consequently, the original LiDe approach should be corrected (cLiDe). Then, the above exploration inspires us to derive a simple linear operator and develop an efficient version of the cLiDe approach, named the sLiDe method, so that the computational speed can be greatly enhanced while maintaining the same high estimation accuracy as cLiDe. Extensive experiments also validate the superiority of our proposed method over the classic approaches. suggesting the significant potential of sLiDe to enable long-term physiological monitoring using radar.

Recursive techniques have demonstrated excellent computational efficiency in classic remote sensing tasks but are rarely applied in emerging remote sensing tasks in the IoT field, such as bioradar-based remote sensing of vital signs (VS). As a fundamental but important problem, VS sensing lacks an effective state model, making it difficult to implement using recursive techniques for a bioradar system. However, this paper presents an efficient recursive technique (BERT) that benefits remote VS sensing and accelerates computational speed with bioradar. Specifically, the recursive technique is derived from an efficient Markov state model based on the features of bioradar VS and is significantly helpful for algorithm implementation. This technique fills the recursive application gap in the VS sensing task and motivates further exploration of its rationale. Thanks to the strong capability of the recursive technique in computation, the proposed BERT requires less time and demonstrates greater accuracy compared to other methods in simulation results. We further conduct extensive experiments on two real datasets — one comprising 50 children and the other involving 30 adults across various scenarios, and the results show that the BERT algorithm significantly accelerates computation speed, reducing processing time by nearly 41% compared to the state-of-the-art algorithm, while maintaining superior estimation accuracy. Furthermore, this work also offers a fresh perspective on interpreting remote VS sensing for bioradar.

The radar imaging method, based on matched filtering (MF), generates high gratings and sidelobes in sparse aperture data, resulting in artifacts in the radar image. The theory of compressive sensing (CS) has brought a breaking change to radar imaging, and imaging enhancement can be realized by exploiting the sparsity of the target image. However, traditional sparse imaging methods ignore the correlation between scatterers. This leads to difficulties in accurately extracting the target’s shape contour and structural features. Thus, in this paper, a convolutional reweighted model based on structured sparsity features is proposed. Specifically, a dynamically relaxing threshold is achieved through the convolutional reweightedl1norm, promoting the sparsity of clustered structures in radar images. Furthermore, to avoid large-scale matrix inversion, the issue is respectively addressed through the alternating direction method of multipliers (ADMM) joint gradient descent framework and linearization approximation approach. In addition, the priori information of MF is utilized to adaptively update the imaging support set during the iteration process, aiming to reduce the data storage pressure. Finally, a large number of simulation and experimental results confirm the generality of the proposed algorithms for radar data in different frequency bands, as well as their superiority in terms of computational efficiency and image quality.

Measuring vital signs (VS) contained in the echoes is crucial to the analyses of breathing and heartbeat signals using medical radar. Although many advanced signal processing algorithms have been developed for radar-based VS measurement and make some improved progress, existing schemes cannot achieve a good estimation of echo phases modulated by the respiratory and cardiac activities with high accuracy or low computation, and thus resulting in serious performance degradation on the subsequent separation of breathing and heartbeat patterns as well as the assessment of breathing rate (BR), heart rate (HR), and heart rate variability (HRV). In this paper, we propose a simple yet effective method to measure VS for medical radar, named 3M method. Specifically, our method firstly introduces the Markov-Gauss model to obtain the recursive expression of the echo phases carrying VS, and secondly derive a simple observation equation (SOE) to reflect the relationship between the observed signal and VS of radar measurement. Thirdly, the aforementioned Markov-Gauss model and SOE are fused by Kalman filter to measure VS with accurate estimation. The 3M method demonstrates an elegant structure, low complexity and excellent features introduced by Kalman filter. Simulation results show the superiority of 3M over other methods. Then, we conduct extensive experiments with insightful visualizations to validate the effectiveness of the 3M method. Comparative results on different scenarios illustrate that the 3M method not only achieves state-of-the-art VS measurement performance but also expresses robust properties to HRV analysis.

It is of great importance to remotely sense trapped victims with radio signals in modern search and rescue after natural disasters like earthquakes, avalanches, building collapses, and so on. Various radio sensors have been developed to date; however, they are hardly deployed to recognize efficiently the vital sign in long-distance, deep-coverage, and multi-subject situations because the back-scattered victim-critical radio signals are really weak and are nearly drowned in the ambient nonstationary noise and clutter. To tackle the formidable difficulty, we present a four-dimensional wideband microwave radar operating at 1.7 GHz to 2.7 GHz and develop a spatio-temporal processing algorithm to fully explore the vital knowledge of victims in the three-dimensional spatial and one-dimensional temporal information. We conducted comprehensive field experiments in real post-disaster environments and demonstrated experimentally that our radio sensor can continuously monitor multiple survivors trapped under mounds of debris in real urban environments. Moreover, we demonstrate that the presented method can achieve a signal-to-noise-and-clutter ratio improvement of more than 20 dB even in the case of deep burial, which enables localizing the victims trapped in the order of ten meters and recognizing the survivors’ vital states. We expect that the presented strategy may open an avenue for future remote life-rescuing and beyond in practical applications.

In recent years, the location and detection of concealed objects and buried targets extensively employed low-frequency MIMO radar, because of its excellent penetration characteristics and high resolution. The environmental parameters are usually unknown, autofocusing method is always used to obtain accurate and clear target images and estimate the environmental parameters at the same time. However, the common disadvantage of the autofocusing methods is the computation burden caused by iterative imaging to estimate the environment parameters. This letter proposes a 3-D image filter method for MIMO array radar, which improve computational efficiency while ensuring the focusing degree and accurate target positioning. Simulation and experimental results show the effectiveness and robustness of the proposed method.

Limited by the total length, the total number of the antenna units as well as their topology, the radar images always suffered from the sidelobe/grating lobe which severely impacts the quality of the radar images. In this article, a convolutional neural network (CNN)-based radar image-enhancing method is proposed. Using the original radar images as the input samples and using their corresponding ideal radar images with no sidelobe/grating lobe as the label to train the CNN. A well-trained CNN can suppress the sidelobe/grating lobe in the radar images. The structure of the specific CNN, the generation methods of the samples and the labels, the training procedure of the CNN, as well as some other detailed implementation strategies are specifically illustrated in this article. The proposed method is utilized to suppress the sidelobe/grating lobe in both the simulated and real recorded radar images. Compared to other existing methods, the proposed method is with better sidelobe/grating lobe suppressing performance and better robustness.

The fundamental difficulties in the supervised deep learning algorithm are obtaining large-scale labeled data and generalizing the trained model to a new environment. In this letter, we propose an unsupervised domain adaption method for human activity classification using micro-Doppler signatures. We study on how to classify micro-Doppler signatures in a new domain using only labeled samples from a different domain, mainly focus on simulation-to-real-world deep domain adaptation. First, we use motion capture (MOCAP) database to generate simulated micro-Doppler data to train the convolutional neural network (CNN). Then, considering the difference between simulation and real-world domain distributions, we introduce a domain discriminator to pit against the feature extractor part of the CNN. Through this adversarial process, like the generative adversarial network, the CNN trained on the simulation domain is able to generalize to the real-world domain. Experiment results show that the proposed method achieves over 84.02% accuracy in real-world micro-Doppler classification, which outperforms nearly 16% in CNN trained on the annotated simulation without domain adaptation and performs better than the existing domain adaptation methods.

Deep neural networks have shown promise in the radar-based human activity analysis application. Different from existing deep learning models that take either micro-Doppler spectrograms or range profiles as their input, the proposed method can process micromotion signatures in a 3-D way. In this letter, we first transform radar echoes into range-Doppler (RD) time points and then directly process the point sets via a designed 3-D network called the RD PointNet. In fact, our point model is a discrete representation of the motion trajectory. Through this quantitative model, we can use the 3-D network to simultaneously capture human motion profiles and temporal variations. The motion capture simulations and ultrawideband radar measurements show that the proposed framework can achieve superior classification accuracy and noise robustness when compared with image-based methods.

The work presented in this letter focuses on the time-variant radio frequency interference (RFI) issue in the low-frequency ultra-wideband (UWB) radar. Different from many previous studies, we first analyze the characteristics of RFIs and scattered echoes in the slow-time dimension and take advantage of overlapped short-time Fourier transform to adapt to the time-variant RFIs and update the frequency Doppler spectrum. Then, in the frequency Doppler spectrum, we adopt the minimum statistic combined with 1-D cell-averaging constant false alarm rate to estimate and separate the RFI power spectrum from the scattered echoes based on their differences. Finally, to mitigate the estimated RFIs, a suboptimal filter controlled by the defined entropy of range profiles after math filtering is designed. Employing a UWB radar, different experiments were conducted, and results verify the proposed method.

A micro-Doppler signal extraction method base on the combination of the CLEAN algorithm and the L-statistics algorithm is proposed. L-statistics algorithm is adapted to estimate the parameter of the local polynomial Fourier transform (LPFT) process. The LPFT process is used to compensate the bulk motion of the target. And then a CLEAN method is used to remove the fuselage echo. Comparing to the L-statistic algorithm, the proposed method can get rid of the fuselage interference without influence on the micro-Doppler part. A simulated data containing fuselage echo, micro-Doppler echo and noise signal is given to verify the validity of the proposed method.

Helicopter classification is still a challenging task for any radar system. This article covers research work about the problem of helicopter classification in time-domain with X-band ground surveillance radar. First, how to project the micro-Doppler characteristics of a helicopter to time-domain echo in a simple way are analyzed and discussed. Fortunately, the detection of invariability patterns in preprocessed data is facilitated by statistics. So, every ‘fingerprint’ corresponding to each feature of preprocessed time-domain echo is calculated. Then, the comparability of Simhash is used to classify helicopters. Furthermore, the experimental results on simulated data prove the validity of the whole analysis and the proposed methods.