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.

Digital radio frequency memory (DRFM)-based main-lobe interrupted sampling repeater jamming (ISRJ) can achieve the effects of both deception and suppression, which cause serious performance degradation in modern radar systems. In this paper, we propose an electronic counter-countermeasure (ECCM) scheme based on signal reconstruction and mismatched filtering (SC-MMF). The ECCM scheme consists of two steps: time-domain signal reconstruction and mismatched filter (MMF) design. First, the time-domain signal without noise is reconstructed using the provided jamming perception information. Then, an MMF corresponding to the reconstructed signal is designed to suppress ISRJ targets. The MMF design problem is formulated as a problem of minimizing the peak false target level (PFTL) and is solved with an improved alternating projection algorithm. After mismatched filtering, ISRJ targets are suppressed. Numerical simulations validate the effectiveness of the proposed method and explore the impacts of various parameters on its performance. Moreover, results obtained on measured data show that the proposed method has practical value in engineering applications.

Early detection of fatigue driving is pivotal for the safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle the above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide the driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about 96%, and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21 bpm , 0.54 bpm , and 2.52 bpm , respectively. And the accuracy of the fatigue detection algorithm we proposed reached 97.63%.

Polyphase sequences with low aperiodic autocorrelation levels, assessed in terms of the integrated sidelobe level (ISL), can be applied in many engineering fields, such as radars, sonars, and code-division multiple access communication systems. In practice, a polyphase sequence that is optimal in some sense (for example, minimum ISL) could be significantly distorted, thus reducing the detection performance of the radar system. Hence in this article, we focus on designing ISL minimization-based polyphase sequences with spectral distortion taken into consideration, where we first derive an expression for the ISL under spectral distortion, i.e., distorted ISL (DISL), and then we propose an iterative algorithm based on majorization-minimization framework to solve the DISL minimization problem. The proposed algorithm is proved to be nonincreasing and is guaranteed to converge to a stationary point. A variety of design results utilizing the proposed algorithm in different scenarios are presented and compared with those by other methods or some known sequences. The proposed algorithm is proved to be effective to design polyphase sequences with low DISL.

Multiple-input multiple-output (MIMO) radar has received significant attention because it offers broad design freedom and superior performance. Polyphase code sets with low autocorrelation sidelobe peaks and low cross-correlation peaks, also known as orthogonal polyphase code sets, can be used as transmission signals by MIMO radar. We present an effective algorithm named as greedy code search based memetic algorithm (MA-GCS) to design such code sets. MA-GCS is a novel variation of the new evolutionary search, which can be used to design only a single binary sequence with low autocorrelation peaks. MA-GCS integrates an evolutionary search for global searches and a greedy code search (GCS) for local searches to improve the accuracy of solutions. Moreover, an accelerating algorithm is designed to reduce the computational complexity of the GCS-based local search. The computational complexity of our algorithm is derived and compared with other algorithms. It is demonstrated by the experimental results that our algorithm is truly effective. On one hand, the code sets designed using our proposed algorithm have better autocorrelation and cross-correlation properties than other methods. On the other hand, the time consumed is acceptable. In addition, the influence of the code set parameters on the performance of the design results is also investigated.

Digital construction relies on effective sensing to enhance the safety, productivity, and quality of its activities. However, current sensing devices (e.g., camera, LiDAR, infrared sensors) have significant limitations in different aspects. In light of the substantial advantages offered by emerging 4D mmw technology, it is believed that this technology can overcome these limitations and serve as an excellent complement to current construction sensing methods due to its robust imaging capabilities, spatial sensing abilities, velocity measurement accuracy, penetrability features, and weather resistance properties. To support this argument, a scientometric review of 4D mmw-based sensing is conducted in this study. A total of 213 articles published after the initial invention of 4D mmw technology in 2019 were retrieved from the Scopus database, and six kinds of metadata were extracted from them, including the title, abstract, keywords, author(s), publisher, and year. Since some papers lack keywords, the GPT-4 model was used to extract them from the titles and abstracts of these publications. The preprocessed metadata were then integrated using Python and fed into the Citespace 6.2.R3 for further statistical, clustering, and co-occurrence analyses. The result revealed that the primary applications of 4D mmw are autonomous driving, human activity recognition, and robotics. Subsequently, the potential applications of this technology in the construction industry are explored, including construction site monitoring, environment understanding, and worker health monitoring. Finally, the challenges of adopting this emerging technology in the construction industry are also discussed.