A low-profile double U-slot microstrip patch antenna with a wide operating bandwidth has been developed in this article. This design produces the dual radiative resonant modes adjacent to each even at the thickness of the antenna of $0.02 {\lambda _{0}}$ . An extremely stable radiation gain that varied from 6.2 to 6.8 dBi is achieved within the whole operating band. In addition, detailed parametric studies in this article provide antenna engineers with simple approximate rules that result in an excellent first-pass design with prescribed characteristics requiring only minimal tuning. Finally, the proposed antenna with the thickness of $0.04 {\lambda _{0}}$ is fabricated and measured. Simulated and measured results are found in good agreement with each other. They have evidently illustrated that the bandwidth is increased by 50% compared to the traditional U-slot antenna. With the help of these simple design procedures and examples in this article, antenna designers could easily develop a low-profile broadband antenna.

In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0–6.3%, the respiration detection was 0–9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.

Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.

Millimeter-wave (mmWave) radar has been widely applied in target detection. However, due to multipath and occlusion, radar often detects ghosts, especially in indoor environments. Existing solutions are mostly tailored to specific, simplified scenarios. To identify radar ghosts in diverse and complex indoor environments, we propose a data-driven approach. A thoughtful indoor radar ghost dataset is created with a multimodal data acquisition and automatic annotation system. And PairwiseNet, an end-to-end deep neural network adept at handling point-pair relationships within sparse point clouds, is proposed for radar ghost recognition. Multi-frame accumulation is also implemented in PairwiseNet. To further enhance PairwiseNet, an additional network incorporating grid maps and U-Net is developed for constructing environmental maps from sequential point clouds. This network is trained through cross-modal distillation, with a depth camera as the teacher. Finally, a series of experiments validate the effectiveness of the proposed method in identifying indoor radar ghosts and autonomously constructing environmental maps. The classification accuracy on the test set reaches 96.0%, accurately identifying ghosts in the vast majority of cases.

Radar has been increasingly used in indoor applications. This paper introduces an indoor tracking system that uses a 24-GHz FMCW radar. Several methods are exploited to alleviate the problem of insufficient resolution caused by low bandwidth. To further distinguish stationary targets, a method based on moving target indicator is proposed. The results show that these methods are effective and two quasi-static targets can be successfully distinguished.

Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost suppression method that integrates multi-target tracking with point cloud deep learning. Our approach consists of four key steps: (1) point cloud pre-segmentation, (2) inter-frame trajectory tracking, (3) trajectory feature aggregation, and (4) feature broadcasting, effectively combining spatiotemporal information with point-level features. Experiments on an indoor dataset demonstrate its superior performance compared to existing methods, achieving 93.5% accuracy and 98.2% AUROC. Ablation studies demonstrate the importance of each component, particularly the complementary benefits of pre-segmentation and trajectory processing.