This paper analyzes broadband antennas, with a special focus on Vivaldi antennas, for their suitability for through-wall radar applications. It assesses various antenna designs, emphasizing high gain, wide impedance bandwidth, and effective wall penetration capabilities. Vivaldi antennas are superior due to their broad bandwidth, high gain, and directional radiation patterns. This study further explores structural optimizations, feeding techniques, and performance enhancement strategies to refine Vivaldi antenna designs for through-wall radar systems. Through a comparative analysis and technical evaluation, this paper highlights Vivaldi antennas’ potential for improving through-wall radar systems’ imaging and sensing capabilities. This presents a pathway for future ultra-wideband advancements.

Human identification using gait as a biometric feature has gained significant attention in recent years, showing notable advancements in medical fields and security. A review of recent developments in remote radar-based gait identification is presented in this article, focusing on the methods used, the classifiers employed, trends and gaps in the literature. Particularly, recent trends highlight the increasing use of Artificial Intelligence (AI) to enhance the extraction and classification of features, while key gaps remain in the area of multi-subject detection. In this paper, we provide a comprehensive review of the techniques used to implement such systems over the past 7 years, including a summary of the scientific publications reviewed. Several key factors are compared to determine the most suitable radar for remote gait-based identification, including accuracy, operating frequency, bandwidth, dataset, range, detection, feature extraction, size and number of features extracted, multiple subject detection, radar modules used, AI used and their properties, and the testing environment. Based on the study, it was determined that Frequency-Modulated Continuous-Wave (FMCW) radars were more accurate than Continuous-Wave (CW) radars and Ultra-Wideband (UWB) radars in this field. Despite the fact that FMCW is the most closely related radar to real-world scenarios, it still has some limitations in terms of multi-subject identification and open-set scenarios. In addition, the study indicates that simpler AI techniques, such as Convolutional Neural Network (CNN), are more effective at improving results.

This paper introduces a novel approach to through-wall Breathing Rate (BR) detection using a custom-built C-Band Frequency Modulated Continuous Wave (FMCW) radar. Operating within the 4–6 GHz frequency range, the radar leverages the C-Band’s ability to penetrate dense barriers, such as 30 cm thick brick and cement walls, with high-resolution capability. Motivated by applications in healthcare and emergency response, this study addresses the need for non-invasive monitoring in scenarios where direct contact is impractical or impossible, such as isolating infectious patients or detecting life in search-and-rescue operations. Unlike traditional methods, this approach incorporates optimized radar settings and an innovative signal processing methodology. This enables accurate measurements without subject movement and allows for real-time applications. Experimental validation involved a diverse dataset of 10 subjects, with radar-based BR measurements compared to ground truth data from the Biopac system. Results demonstrated high accuracy, achieving a mean absolute error of 0.07 BPM and a root mean square error of 0.09 BPM. These findings confirm radar robustness in challenging, real-world scenarios, establishing its potential as a cost-effective, reliable solution for through-wall BR monitoring.

Advancements in radar technologies, including Continuous Wave, Frequency Modulated Continuous Wave, and Ultra-Wideband, are surveyed for their applications in biometric identification and Through-Wall Imaging. This survey focuses on radar technologies used for vital signal detection and gait analysis. A comparison of key parameters across diverse research studies is conducted. Moreover, the benefits of varying radar technologies and Artificial Intelligence algorithms are discussed. The study provides a roadmap for subsequent investigations in the fields of radar technology and biometric identification.

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, the primary challenge lies in the presence of random body movements (BMs), which can significantly hinder the accurate detection of vital signs. To compensate the affected signal in a timely manner, portions of BM must be correctly identified. To address this challenge, this work proposes a solution based on the Density-Based Spatial Clustering of Applications with Noise (DBScan) algorithm to detect the occurrence of BM in radar signals. The main idea of this algorithm is to cluster the radar samples, aiming to differentiate between segments in which the subject is stable and segments in which the subject is moving. Using a dataset involving eight subjects, the proposed method successfully detects three types of body movements: chest movement, body rotation, and arm movement. The achieved results are promising, with F1 scores of 0.83, 0.73, and 0.8, respectively, for the detection of each specific movement type.