Search Results (66)

Millimeter-wave frequencies are crucial for meeting the high-capacity, low-latency demands of 5G communication systems, thereby driving the need for compact, high-gain antenna arrays capable of efficient beamforming. This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 × 6 linear series-fed microstrip patch antenna array for 5G millimeter-wave communication operating at 28 GHz. The proposed antenna is fabricated on a low-loss Rogers RO3003 substrate and incorporates an integrated symmetric two-way microstrip power divider to ensure balanced feeding and phase uniformity across elements. The antenna achieves a simulated peak gain of 11.5 dBi and a broad simulated impedance bandwidth of 30.21%, with measured results confirming strong impedance matching and a return loss better than −20 dB. The far-field radiation patterns demonstrate a narrow, highly directive beam in the E-plane, and the H-plane results reveal beam tilting behavior, validating the antenna’s capability for passive beam steering through feedline geometry and element spacing (~0.5λ). Surface current distribution analysis confirms uniform excitation and efficient radiation, further validating the design’s stability. The fabricated prototype shows excellent agreement with the simulation, with minor discrepancies attributed to fabrication tolerances. These results establish the proposed antenna as a promising candidate for applications requiring compact, high-gain, and beam-steerable solutions, such as 5G mm-wave wireless communication systems, point-to-point wireless backhaul, and automotive radar sensing. Full article

Millimeter-wave (mmWave) antennas and antenna arrays have gained significant attention due to their pivotal role in emerging wireless communication, sensing, and imaging technologies. With the rapid deployment of 5G and the transition toward 6G networks, the demand for compact, high-gain, and reconfigurable mmWave antennas has intensified. This article highlights recent advancements in mmWave antenna technologies, including hybrid beamforming using phased arrays, dynamic beam-steering enabled by liquid crystal and MEMS-based structures, and high-capacity MIMO architectures. We also examine the integration of metamaterials and metasurfaces for miniaturization and gain enhancement. Applications covered include wearable antennas with low-SAR textile substrates, conformal antennas for UAV-based mmWave relays, and high-resolution radar arrays for autonomous vehicles. The study further analyzes innovative fabrication methods such as inkjet and aerosol jet printing, micromachining, and laser direct structuring, along with advanced materials like Kapton, PDMS, and graphene. Numerical modeling techniques such as full-wave EM simulation and machine learning-based optimization are discussed alongside experimental validation approaches. Beyond communications, we assess mmWave systems for biomedical imaging, security screening, and industrial sensing. Key challenges addressed include efficiency degradation at high frequencies, interference mitigation in dense environments, and system-level integration. Finally, future directions, including AI-driven design automation, intelligent reconfigurable surfaces, and integration with quantum and terahertz technologies, are outlined. This comprehensive synthesis aims to serve as a valuable reference for advancing next-generation mmWave antenna systems. Full article

The demographic shift toward an aging population calls for innovative strategies to ensure independence, health, and quality of life in later years. In this context, Ambient Assisted Living (AAL) solutions, supported by Information and Communication Technologies (ICTs), offer promising advances for non-invasive and continuous support. Commonly, ICTs are evaluated only from the perspectives related to key performance indicators (KPIs); nevertheless, the design and implementation of such technologies must account for important psychological, social, and ethical dimensions. Radar-based sensing systems are emerging as an option due to their unobtrusive nature and capacity to operate without direct user interaction. This work explores how radar technologies, particularly those operating in the millimeter wave (mmWave) spectrum, can provide core key value indicators (KVIs) essential to aging societies, such as human dignity, trustworthiness, fairness, and sustainability. Through a review of key application domains, the paper illustrates the practical contributions of mmWave radar in Ambient Assisting Living (AAL) contexts, underlining how its technical attributes align with the complex needs of elderly care environments and produce value for society. This work uniquely integrates key value indicator (KVI) frameworks with mmWave radar capabilities to address unmet ethical needs in the AAL domain. It advances existing literature by proposing a value-driven design approach that directly informs technical specifications, enabling the alignment of engineering choices with socially relevant values and supporting the development of technologies for a more inclusive and ethical society. Full article

Arc scanning synthetic aperture radar (ArcSAR) can achieve high-resolution panoramic imaging and retrieve submillimeter-level deformation information. To monitor buildings in a city scenario, ArcSAR must be lightweight; have a high resolution, a mid-range (around a hundred meters), and low power consumption; and be cost-effective. In this study, a novel high-resolution wide-beam single-chip millimeter-wave (mmwave) ArcSAR system, together with an imaging algorithm, is presented. First, to handle the non-uniform azimuth sampling caused by motor motion, a high-accuracy angular coder is used in the system design. The coder can send the radar a hardware trigger signal when rotated to a specific angle so that uniform angular sampling can be achieved under the unstable rotation of the motor. Second, the ArcSAR’s maximum azimuth sampling angle that can avoid aliasing is deducted based on the Nyquist theorem. The mathematical relation supports the proposed ArcSAR system in acquiring data by setting the sampling angle interval. Third, the range cell migration (RCM) phenomenon is severe because mmwave radar has a wide azimuth beamwidth and a high frequency, and ArcSAR has a curved synthetic aperture. Therefore, the fourth-order RCM model based on the range-Doppler (RD) algorithm is interpreted with a uniform azimuth angle to suit the system and implemented. The proposed system uses the TI 6843 module as the radar sensor, and its azimuth beamwidth is 64°. The performance of the system and the corresponding imaging algorithm are thoroughly analyzed and validated via simulations and real data experiments. The output image covers a 360° and 180 m area at an azimuth resolution of 0.2°. The results show that the proposed system has good application prospects, and the design principles can support the improvement of current ArcSARs. Full article

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system. Full article

This review paper provides a comprehensive examination of millimeter-wave radar technologies in automotive systems, reviewing their advancements through signal processing innovations. The evolution of radar systems, from conventional platforms to mmWave technologies, has significantly enhanced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Signal processing advancements, including constant false alarm rate detection, multiple-input–multiple-output systems, and machine learning-based techniques, are explored for their roles in improving radar performance under dynamic and challenging environments. The integration of mmWave radar with complementary sensing technologies such as LiDAR and cameras facilitates robust environmental perception essential for advanced driver-assistance systems and autonomous vehicles. This review also calls attention to key challenges, including environmental interference, material penetration, and sensor fusion, while addressing innovative solutions such as adaptive signal processing and sensor integration. Emerging applications of joint communication–radar systems further presents the potential of mmWave radar in autonomous driving and vehicle-to-everything communications. By synthesizing recent developments and identifying future directions, this review stresses the critical role of mmWave radar in advancing vehicular safety, efficiency, and autonomy. Full article

The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable low noise amplifier (LNA) and a single pole double throw (SPDT) switch. The HARRF architecture supports both X-band (8–12 GHz) and K/Ka-band (23–28 GHz) operations, enabling seamless adaptation between radar, satellite communication, and millimeter-wave (mmWave) 5G applications. The proposed receiver front-end employs a 0.15 μ$\mathrm{m}$ pseudomorphic high electron mobility transistor (pHEMT) process, optimised through a three-stage cascaded LNA topology. A switched-tuned matching network is utilised to achieve reconfigurability between X-band and K/Ka-band. Performance evaluations indicate that the X-band LNA achieves a gain of 23–27 dB with a noise figure below 7 dB, whereas the K/Ka-band LNA provides 23–27 dB gain with a noise figure ranging from 2.3–2.6 dB. The SPDT switch exhibits low insertion loss and high isolation, ensuring minimal signal degradation across operational bands. Network analysis and scattering parameter extractions were conducted using advanced design system (ADS) simulations, demonstrating superior return loss, power efficiency, and impedance matching. Comparative analysis with state-of-the-art designs shows that the proposed HARRF outperforms existing solutions in terms of reconfigurability, stability, and wideband operation. The results validate the feasibility of the proposed reconfigurable RF front-end in enabling efficient spectrum utilisation and energy-efficient transceiver systems for next-generation communication networks. Full article

Drones are increasingly being used for a variety of applications, but their use can be hampered when Global Navigation Satellite System (GNSS) signals are unavailable. This paper presents Terrain-Referenced Navigation (TRN) from a class 1 drone using a low-cost, automotive-class mm-wave radar to enable accurate navigation in GNSS-denied environments. The radar-derived elevation profile of the ground beneath the drone as it flies is compared to a light detection and ranging (lidar)-based digital terrain map, and using a particle filter, the drone’s position is estimated. Results show that mm-wave radar TRN can provide an accurate navigation capability in the absence of GNSS and offers several advantages over other alternative navigation technologies. Full article

Human activity recognition (HAR) is an important field in the application of millimeter-wave radar. Radar-based HARs typically use Doppler signatures as primary data. However, some common similar human activities exhibit similar features that are difficult to distinguish. Therefore, the identification of similar activities is a great challenge. Given this problem, a recognition method based on convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and class activation mapping (CAM) is proposed in this paper. The spectrogram is formed by processing the radar echo signal. The high-dimensional features are extracted by CNN, and then the corresponding feature vectors are fed into the BiLSTM to obtain the recognition results. Finally, the class activation mapping is used to visualize the decision recognition process of the model. Based on the data of four similar activities of different people collected by mm-wave radar, the experimental results show that the recognition accuracy of the proposed model reached 94.63%. Additionally, the output results of this model have strong robustness and generalization ability. It provides a new way to improve the accuracy of human similar posture recognition. Full article

This paper presents the clustering and classification of the radar scattering characteristics of vehicles under real-world driving conditions. The classification of 14 distinct vehicle types is achieved through statistical features derived from their radar cross-section (RCS) characteristics, represented as histograms. Various machine learning classification techniques are applied, and their performance is evaluated across different clustering scenarios. The results of the clustering algorithm are in line with the physics-based expectations on the scattering from different vehicle types. The classification results demonstrate the effectiveness of the proposed algorithm, validating the histogram-based feature method as a novel and promising approach for vehicle identification and detection. In addition, the results highlight the potential applications of our methods in millimeter-wave (mmWave) radar technology, illustrating their capability to improve feature extraction by means of RCS histograms and ensure robust classification in diverse and challenging environments. Full article

Between 2018 and 2024, the global market has experienced significant advancements in sensor technologies for monitoring patients’ health conditions, which have demonstrated a pivotal role in diagnostics, treatment monitoring, and healthcare optimization. Progress in microelectronics, device miniaturization, and wireless communication technologies has facilitated the development of sophisticated sensors, including wearable devices such as smartwatches and fitness trackers, enabling the real-time monitoring of key health parameters. These devices are widely employed across clinical settings, nursing care, and daily life to collect critical data on vital signs, including heart rate, blood pressure, oxygen saturation, and respiratory rate. A systematic review of the developments within this period highlights the transformative potential of AI and IoT-based technologies in healthcare personalization, particularly in disease symptom prediction and public health management. Furthermore, innovative techniques such as respiratory inductive plethysmography (RIP) and millimeter-wave radar systems (mmTAA) have emerged as precise, non-contact solutions for respiratory monitoring, with applications spanning diagnostics, therapeutic interventions, and enhanced safety in daily life. Full article

In this paper, we present a low-cost, low-power millimeter-wave (mmWave) skeletal joint localization system. High-quality point cloud data are generated using the self-developed BHYY_MMW6044 59–64 GHz mmWave radar device. A sliding window mechanism is introduced to extend the single-frame point cloud into multi-frame time-series data, enabling the full utilization of temporal information. This is combined with convolutional neural networks (CNNs) for spatial feature extraction and a bidirectional gated recurrent unit (BiGRU) for temporal modeling. The proposed spatio-temporal information fusion framework for multi-frame point cloud data fully exploits spatio-temporal features, effectively alleviates the sparsity issue of radar point clouds, and significantly enhances the accuracy and robustness of pose estimation. Experimental results demonstrate that the proposed system accurately detects 25 skeletal joints, particularly improving the positioning accuracy of fine joints, such as the wrist, thumb, and fingertip, highlighting its potential for widespread application in human–computer interaction, intelligent monitoring, and motion analysis. Full article

This paper introduces a high-resolution 77–81 GHz mmWave Synthetic Aperture Radar (SAR) imaging methodology integrating low-cost hardware with modified radar signal characteristics specifically for NDT applications. The system is optimized to detect minimal defects in materials, including low-reflectivity ones. In contrast to the existing studies, by optimizing key system parameters, including frequency slope, sampling interval, and scanning aperture, high-resolution SAR images are achieved with reduced computational complexity and storage requirements. The experiments demonstrate the effectiveness of the system in detecting optically undetectable minimal surface defects down to 0.4 mm, such as bonded adhesive lines on low-reflectivity materials with 2500 measurement points and sub-millimeter features on metallic targets at a distance of 30 cm. The results show that the proposed system achieves comparable or superior image quality to existing high-cost setups while requiring fewer data points and simpler signal processing. Low-cost, low-complexity, and easy-to-build mmWave SAR imaging is constructed for high-resolution SAR imagery of targets with a focus on detecting defects in low-reflectivity materials. This approach has significant potential for practical NDT applications with a unique emphasis on scalability, cost-effectiveness, and enhanced performance on low-reflectivity materials for industries such as manufacturing, civil engineering, and 3D printing. Full article

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is $3.98\pm 5.81$ mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is $6.52\pm 7.51$ mmHg. Full article

Frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar systems are increasingly utilized in environmental sensing due to their high range resolution and robust sensing ability in severe weather environments. However, mutual interference among radar systems significantly degrades the target detection capability. Recent advancements in interference mitigation utilizing deep learning (DL) approaches have demonstrated promising results. DL-based approaches typically have high computational costs, which makes them unsuitable for real-time applications with strict latency requirements and limited computing resources. In this paper, we propose an efficient solution for real-time radar interference mitigation. A lightweight transformer, which is smaller and faster than the baseline transformer, is designed to reduce interference. The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. In addition, a two-stage knowledge distillation (KD) process is deployed to compress the network and enhance its efficiency. The staged distillation approach alleviates the training difficulties associated with substantial differences between the teacher and student networks. Both simulated and real-world experiments demonstrate that the proposed method outperforms the state-of-the-art methods while achieving high processing speeds. Full article