Noncontact vital-sign telemonitoring by radar systems is applied more widely. When considering the implementation of Doppler radar for more precise extraction of cardiac physiological parameters to adapt to clinical diagnosis of heart diseases, the most open heart rate (HR) detection methods based on the periodic information are far from enough. Here, this article presents a new simple, effective, and high-accuracy settlement to maintain cardiac activity sensing comprehensively and systematically for the first time, for the estimation of beat-to-beat intervals (BBIs) and cardiac timings. We propose the decoding peak detection (DPD) method to address heartbeat peaks’ extraction problem by decoding the most likely sequence of states from the single-band frequency envelogram (FEnv) of the radar signal, thus realizing HR variability analysis. Furthermore, a hidden semi-Markov model (HSMM) is utilized to detect cardiac timings in bandpass filtered radar data, which is equivalent to positions of the R-peak and end-T-wave carried in electrocardiogram (ECG). Experimental results show that the proposed method achieves an average $F_{1}$ score of 93.19% ± 0.73% with the ECG as gold-standard, evaluated on a data set from six subjects. The accuracy of radar-based extracted BBIs is assessed by the mean relative error, which ranges from 0.51% to 1.06% by HSMM and 0.37% to 1.15% by DPD, respectively. Finally, a series of experiments are conducted to confirm the high agreement between radar signals and cardiac activities. Meanwhile, from the cardiac timings analysis by the proposed methods, we can find the advantage of radar-based detection at some specific timings compared with ECG-based, e.g., end-T-wave detection.

Human identification based on radar signatures of individual heartbeats is crucial in various applications, including user authentication in mobile devices, identification of escaped criminals, etc. Usually, optical systems employed to recognize humans are sensitive to ambient light environments, while radar does not have such a drawback, since it has high penetration and all-weather capability. Meanwhile, since micro-Doppler characteristics from the heart of different people are distinct and not easy to fake, it can be used for identification. In this paper, we employed a deep convolutional neural network (DCNN) and conventional supervised learning methods to realize heartbeat-based identification. First, the heartbeat signals were acquired by a Doppler radar and processed by short-time Fourier transform. Then, predefined features were extracted for the conventional supervised learning algorithms, while time–frequency graphs were directly inputted to the DCNN since the network had its own feature extraction part. It is shown that the DCNN could achieve average accuracy of 98.5% for identifying four people, and higher than 80% when the number of people was less than ten. For conventional supervised learning algorithms when identifying four people, the accuracy of the support vector machine (SVM) was 88.75%, and the accuracy of SVM–Bayes was 91.25%, while naive Bayes had the lowest accuracy of 80.75%.

Deep learning (DL) is widely used in automatic target recognition (ATR) of synthetic aperture radar (SAR) images. Related researches show that DL models for SAR ATR are vulnerable to adversarial examples attack in the digital domain. However, how to generate adversarial examples in practical scenarios is critical and challenging. In this paper, we propose a systematic SAR perturbation generation algorithm (SAR-PeGA) for target recognition network. Firstly, assuming that some reflection phase tuning samples are located in the fixed area of SAR target, we adjust the phase characteristics of reflected signal with variable phase sequences. Secondly, we take the imperceptible perturbations from universal adversarial perturbations (UAP) as reference. Then, we construct the unconstrained minimum optimization model to find the specific phase sequences of tuning samples, and optimize the model with the adaptive moment estimation (Adam) optimizer. Finally, SAR adversarial examples can be flexibly generated through the proposed deceptive jamming model. Experimental results demonstrate that the proposed method can generate imperceptible jamming and effectively attack three classical recognition models.

Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar‐based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy. Previous researches have revealed that gait signatures acquired using radar can be used for human identification, but there is almost no literature on the long‐term stability of gait signatures. Due to the long‐term interval observation, the human micro‐Doppler will change according to the subject (such as slight differences in walking posture). In this study, a novel network is proposed to realise stable identification of humans by extracting long‐term stable features. The micro‐Doppler data is processed by a short‐time Fourier transform and finally classified by the proposed neural network. Data acquisition was carried out within more than a month. The experimental results demonstrate that the recognition accuracy of the validation set can reach about 99%, and the recognition accuracy of the test set can reach 90% (improved 3% compared with the network without a recurrent neural network), showing the potential of the proposed method in long‐term stable identification.

Achieving high angular resolution estimation is essential for enhancing environmental perception and accurately detecting extended targets. However, implementing a large aperture radar system as a single sensor faces significant technical and economic challenges. Radar networks, comprising multiple individual radar sensors, offer a promising solution to these obstacles. In this article, we propose a method for high resolution direction-of-arrival (DOA) estimation tailored for coherent distributed millimeter-wave radar systems. Our approach leverages multiple-input multiple-output technology and distributed arrays to construct a virtual sparse array capable of synthesizing a full uniform array covariance matrix through specific correlation transformation. Furthermore, we introduce a joint optimization method for directly angle finding that efficiently addresses large-scale optimization problems by exploiting both sparsity and low-rank properties, thereby circumventing the traditional sequential steps of matrix reconstruction and DOA estimation. We employ the alternating direction method of multipliers to ensure reliable convergence. Our proposed model and method have been extensively validated through comprehensive simulations, highlighting their advantages within the realm of distributed radar systems.

The rapid advancement of Internet of Things (IoT) technology, particularly in the realm of autonomous driving, has elevated the requirements for automotive radar systems to achieve precise environmental perception. This paper delves into the distributed MIMO radar network model and the associated signal processing techniques that enable accurate measurement of range, velocity, and angular positions, culminating in the generation of high-resolution 4D point clouds. These capabilities are pivotal for intelligent interactions between vehicles and their surroundings within the IoT ecosystem. We introduce a stepped-frequency frequency-modulated continuous wave (SF-FMCW) waveform that incrementally increases the starting frequency of each chirp, leading to a larger bandwidth and finer range resolution without altering the individual chirp bandwidth. Furthermore, we propose a hybrid FDM-DDM scheme to ensure orthogonality among MIMO waveforms. This scheme allows for decoding across various DDM modes through the application of overlapping binary masks, while maintaining unambiguous range-Doppler measurements, which is crucial for real-time data processing and decision-making within IoT. To enhance angular resolution, we optimize the array configuration and develop a low sidelobe direction of arrival (DOA) estimation method using phase coherence factor (PCF) techniques. Extensive simulations and experimental analyses demonstrate the superior performance of the proposed methods in resolving closely spaced targets and generating high-fidelity 4D point clouds, even in challenging scenarios with limited angular separation. The development of these technologies is significant for intelligent perception and safe navigation of vehicles within the IoT, providing a technological foundation for seamless integration of vehicles with the IoT infrastructure.