Normal human heartbeat and respiratory signals are not steady sinusoidal signals and contain a multitude of harmonic components. The principle of vital sign detection using millimeter-wave radar is based on monitoring the chest cavity’s movements to obtain vital sign signals. Due to the amplitude of the respiratory signal being an order of magnitude higher than that of the heartbeat signal, the higher-order harmonics of the respiratory signal, particularly the second and third harmonics, which fall within the frequency range of the heartbeat signal, can easily overshadow it. This overlap creates difficulty in separating respiratory and cardiac signals using traditional methods. This paper proposes a heartbeat signal extraction method based on template selection, which extracts the heartbeat signal during respiratory pauses, suppresses the interference of respiratory harmonics, and experimentally validates the effectiveness of the method.

Deep learning methods have been widely used in the field of SAR target detection, but conventional deep neural networks do not have the ability to preserve old knowledge. With new classes of SAR images constantly being acquired, typically we re-train the network using all the data, which puts a burden on storage and computing. However, if the model updates using only new data, the detector will catastrophically forget previous knowledge. To address this problem, this paper proposes a Class-incremental learning (CIL) method for SAR multi-class target detection, based on an overall framework of knowledge distillation (KD), which enables to maintain the ability of the detector to detect old classes of targets whilst learning new knowledge. The method includes two core components: First, we introduce the Localization Distillation (LD) into the incremental learning framework, preventing the impact of multi-scale SAR target introduction on small target detection capability during the incremental learning process; Second, we apply the global relationship module (GR) to the feature-review process, which strengthens the learning of target-background correlation. We performed comparison experiments on the MSAR-1.0 SAR multi-class target detection dataset, and our method has better performance compared with several mainstream incremental detection methods.

This paper presents a comprehensive study on the application of Frequency-Modulated Continuous-Wave (FMCW) radar in vital sign monitoring. We identify and address the limitations of traditional signal-to-noise ratio (SNR) metrics in human vital signs detection field. Recognizing the challenges posed by the human body’s orientation, distance from the radar, and random body movements, we propose an innovative SNR estimation method. Our approach considers various real-world scenarios, including different target distances, angles, and levels of body movement, to ensure the robustness and effectiveness of vital sign detection. Through a series of experiments, we demonstrate the efficacy of our novel SNR algorithm, underscoring its superiority in different situational contexts compared to conventional methods.

To enable the synthetic aperture radar (SAR) automatic target recognition (ATR) system to continuously adapt to new recognition scenarios, it is necessary to equip the system with the ability to quickly update models. However, when these models learn new tasks, the knowledge of old tasks is quickly forgotten, a phenomenon known as catastrophic forgetting. The reason for catastrophic forgetting is that the model does not use the features of old tasks sufficiently. In this letter, an exemplar-free class incremental learning based on maximizing mutual information (CIL-MMI) is proposed to solve this problem. To effectively use the extracted features, CIL-MMI actively clusters features to maximize the mutual information (MI) between features and corresponding labels. The proposed method successfully avoids the distribution overlap caused by the small interclass differences and large intraclass variances inherent in SAR images. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset indicate that the proposed method outperforms state-of-the-art approaches, demonstrating improvements of 5.41%, 1.93%, and 2.47% at incremental steps 1, 2, and 3, respectively.

The gradual increase of SAR imagery often accompanies the appearance of new targets, but traditional detection frameworks can only detect existing target classes and cannot detect new. Typically, we must update the model using both new and old data, which puts a strain on storage and computation, but if we only update with new data, the detection performance on old classes will suffer dramatically. For this reason, this paper proposes to use the continual learning (CL) method to solve the problem of SAR target incremental detection. Mainstream CL methods generally consider localization to be a class-irrelevant function, however, this strategy is unsuitable for SAR imagery with significant background changes, leading to poor detection performance. Addressing the above issues, this paper proposes a continual learning object detection (CLOD) method, with an overall framework based on knowledge distillation. The focus of the methodology consists of two parts: firstly, we introduce the predicted location probability representation (PLPR) method, by using spatial discretization and segmented probabilistic statistics, to transform the localization results into probability distributions, thus allowing the localization function to participate in the continual learning process; secondly, we design a proposal selection strategy according to the background characteristics of SAR images, which improves the quality of the proposals during the knowledge review to further optimize the learning effect. Experiments on the latest multi-class SAR target detection dataset MSAR-1.0, show that our method is able to learn new knowledge with less performance penalty for old classes than other methods. In multiple data incremental settings, our method provides a 2%-11% performance improvement over numerous common methods.

Recently, people have become more concerned about life and health, and are paying more attention to the detection of their vital signs indicators. Non-contact detection of respiration rate(RR) and heart rate (HR) by using millimeter-wave (mmWave) radar is common. In this article, we propose a detection system based on multi-vital box fitting approximation using millimeter wave radar. On the basis of using different vital boxes signals with a certain period correlation, a new vital signal can be obtained by joint multi-boxes fitting approximation to a new vital signal, and taking measurements of respiration rate and heart rate. The experimental results show that the accuracy of the system using the multi-vital box vital signal fitting approximation improves about 3% for the detection of RR and about 2% for the detection of HR than the traditional single vital box system.

Vital signs monitoring technology is an important part of modern healthcare. With the continuous advancement of medical technology, the existing vital signs monitoring technology can no longer meet people’s needs. Through the analysis of different application scenarios, it can be seen that the new vital signs monitoring technology presents a trend of non-sensory monitoring, long-term monitoring, bed monitoring, and early diagnosis. Remote vital signs monitoring using millimeter wave radar has the advantages of non-contact, continuous and high degree of freedom, and can be used to monitor the vital signs of special patients. However, the signal received by mm-wave radar are very sensitive to random body movements, which reduces the accuracy of heart rate and respiratory rate. To overcome this challenge, we propose a method based on chest mechanical motion modeling to remove random body movements.