Search Results (155)

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

In this study, a novel improved ultra-wideband (UWB) antipodal Vivaldi antenna suitable for breast cancer detection via microwave imaging was designed. The antenna was made more directional by adding three pairs of nestings to the antenna fins by adding elliptical patches. The frequency operating range of the proposed antenna is UWB 3.6–13 GHz, its directivity is 11 dB, and its gain is 9.27 dB. The antenna is designed with FR4 dielectric material and dimensions of 34.6 mm × 33 mm × 1.6 mm. It was demonstrated that the bandwidth, gain, and directivity of the proposed antenna meet the requirements for UWB radar applications. The Vivaldi antenna was tested on an imaging system developed using the CST Microwave Studio (CST MWS) program. In CST MWS, a hemispherical heterogeneous breast model with a radius of 50 mm was created and a spherical tumor with a diameter of 0.9 mm was placed inside. A Gaussian pulse was sent through Vivaldi antennas and the scattered signals were collected. Then, adaptive Wiener filter and image formation algorithm delay-multiply-sum (DMAS) steps were applied to the reflected signals. Using these steps, the tumor in the breast model was scanned at high resolution. In the simulation application, the tumor in the heterogeneous phantom was detected and imaged in the correct position. A monostatic radar-based system was implemented for scanning a breast phantom in the prone position in an experimental setting. For experimental measurements, homogeneous (fat and tumor) and heterogeneous (skin, fat, glandular, and tumor) breast phantoms were produced according to the electrical properties of the tissues. The phantoms were designed as hemispherical with a diameter of 100 mm. A spherical tumor tissue with a diameter of 16 mm was placed in the phantoms produced in the experimental environment. The dynamic range of the VNA device used allowed us to image a 16 mm diameter tumor in the experimental setting. The developed microwave imaging system shows that it is suitable for the early-stage detection of breast cancer by scanning the tumor in the correct location in breast phantoms. Full article

This paper presents the design, implementation, and testing of a traffic monitoring platform based on 5G-connected Ultra-Wideband (UWB) radars deployed on a university campus. The development of both connected radars and an IoT platform is detailed. The connected radars integrate commercial components, including a Raspberry Pi (RPi), a UWB radar, a standard enclosure, and a custom communication board featuring a 5G module. The IoT platform, which receives data from the radars via MQTT, is scalable, easily deployable, and supports radar management, data visualization, and external data access via an API. The solution was deployed and tested on campus, demonstrating real-time operation over a commercial 5G network with an estimated median latency between the radar and server of 75 ms. A preliminary evaluation conducted on a single radar during peak-hour traffic on a double-lane road, representing a challenging scenario, indicated a high detection rate of 94.81%, a low false detection rate of 1.02%, a high classification accuracy of 97.29%, and a high direction accuracy of 99.66%. These results validate the system’s capability to deliver accurate traffic monitoring. Full article

Ultra-wide-band (UWB) radar technology is a contactless signal detection technology. The UWB technology can be used for the clinical medical monitoring of the human heartbeat and respiration. In this paper, a novel algorithm is proposed to estimate heart rate (HR) and respiratory rate (RR) depending on the received UWB radar signal. The proposed algorithm initially applies Singular Spectrum Analysis (SSA) to remove noise from the received UWB radar signal, and then applies Variational Mode Decomposition (VMD) to effectively separate the respiratory and heartbeat components. To address the issue of heartbeat components being susceptible to harmonic and inter-modulation components interference, a double fuzzy logic estimation is implemented to achieve robust real-time extraction of heart rate. In this paper, extensive experiments are conducted at various distances and angles. The experimental results of the SSA-VMD-Double-Fuzzy-Logic (SVDF) method have been compared with other methods, demonstrating its effectiveness and the advantages of the SVDF proposed in this paper. Full article

Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging. Full article

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. Full article

Non-contact human respiration rate monitoring can be used for sleep apnea detection and home care. Typically, the human body does not remain stationary for long periods, and body movement can significantly affect the performance of non-contact respiratory monitoring. Because the breathing rate generally remains stable over short periods, using measurements from only a portion of the radar echo signals does not result in significant errors, and these errors will be smaller than those caused by body movement. However, selecting a window size that is too short reduces frequency resolution, leading to increased estimation errors. Choosing an appropriate window length can improve estimation accuracy. In this paper, we propose an algorithm to determine whether the subject is stationary and select the received signal with minimal body movement. Experimental results are compared using alternative schemes, including fast Fourier transform (FFT), short-time Fourier transform (STFT), and RGB-D camera-assisted methods, in terms of root mean square error (RMSE) performance. Full article

Ground-Penetrating Radar (GPR) systems with ultra-wideband (UWB) antennas introduce the benefits of both high and low frequencies. Higher frequencies offer finer spatial resolution, enabling the detection of small-scale features and details, while lower frequencies improve depth penetration by minimising signal attenuation, allowing the system to explore deeper subsurface layers. This combination optimises the performance of GPR systems by balancing the need for detailed imaging with the requirement for deeper penetration. This work presents the design of a wideband inverted U-shaped patch antenna with a wide rectangular slot centred at a frequency of 1.5 GHz. The antenna is fed through a microstrip feed line and employs a partial ground plane. Through simulation, the antenna is optimised by varying the patch dimensions and slot size. Further modifications to the partial ground plane improve the UWB and gain characteristics of the antenna. The optimised antenna is fabricated using a double-sided copper-clad FR4 substrate with a thickness of 1.6 mm and characterised using a Vector Network Analyser (VNA), with final dimensions of 200 mm × 300 mm. The experimental results demonstrate a return loss below −10 dB across the operational band from 1.068 GHz to 4 GHz and a maximum gain of 7.29 dB at 4 GHz. In addition to other bands, the antenna exhibits a return loss consistently below −20 dB in the frequency range of 1.367 GHz to 1.675 GHz. These results confirm the antenna’s UWB performance and its suitability for GPR applications in utility mapping, landmine and artefact detection, and identifying architectural defects. Full article

The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machine learning (ML) to LOS/NLOS classification and this paper is part of this trend, but not all the different aspects of ML have been analyzed. In the general ML domain, poisoning and adversarial attacks and the related mitigation techniques are an active area of research. Such attacks aim to hamper the ML classification process by poisoning the data set. Mitigation techniques are designed to counter this threat using different approaches. Poisoning attacks in LOS/NLOS classification have not received significant attention by the wireless communication community and this paper aims to address this gap by proposing the application of a specific mitigation technique based on outlier detection algorithms. The rationale is that poisoned samples can be identified as outliers from legitimate samples. In particular, the study described in this paper proposes a recent outlier detection algorithm, which has low computing complexity: the sparse data observers (SDOs) algorithm. The study proposes a comprehensive analysis of both conventional and novel types of attacks and related mitigation techniques based on outlier detection algorithms for UltraWideBand (UWB) channel classification. The proposed techniques are applied to two data sets: the public eWINE data set with seven different UWB LOS/NLOS different environments and a radar data set with the LOS/NLOS condition. The results show that the SDO algorithm outperforms other outlier detection algorithms for attack detection like the isolation forest (iForest) algorithm and the one-class support vector machine (OCSVM) in most of the scenarios and attacks, and it is quite competitive in the task of increasing the UWB LOS/NLOS classification accuracy through sanitation in comparison to the poisoned model. Full article

A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s). It is demonstrated that the FPGA data-processing speed matches that of the radar output, thus eliminating data loss. The radar system achieves a remarkable speed of over 9000 waveforms per second on each channel. The proposed architecture is scalable to accommodate higher sampling rates and various waveform periods. It is also multi-functional since the FPGA controls and synchronizes two transmitters and a dual-channel receiver, performs signal reconstruction on both channels simultaneously, and carries out user-defined averaging, trace windowing, and interference suppression for improving the receiver’s signal-to-noise ratio. We also investigate the throughput rate while offloading radar data onto an external device through an Ethernet link. Since the radar data rate significantly exceeds the Ethernet link capacity, we show how the FPGA-based averaging and windowing functions are leveraged to reduce the amount of offloaded data while fully utilizing the radar output. Full article

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB). Full article

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RRs and CRs even in the presence of body movement. Full article

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%. Full article

Mental distress-induced imbalances in autonomic nervous system activities adversely affect the electrical stability of the cardiac system, with heart rate variability (HRV) identified as a related indicator. Traditional HRV measurements use electrocardiography (ECG), but impulse radio ultra-wideband (IR-UWB) radar has shown potential in HRV measurement, although it is rarely applied to psychological studies. This study aimed to assess early high levels of mental distress using HRV indices obtained using radar through modified signal processing tailored to reduce phase noise and improve positional accuracy. We conducted 120 evaluations on 15 office workers from a software startup, with each 5 min evaluation using both radar and ECG. Visual analog scale (VAS) scores were collected to assess mental distress, with evaluations scoring 7.5 or higher classified as high-mental distress group, while the remainder formed the control group. Evaluations indicating high levels of mental distress showed significantly lower HRV compared to the control group, with radar-derived indices correlating strongly with ECG results. The radar-based analysis demonstrated a significant ability to differentiate high mental distress, supported by receiver operating characteristic (ROC) analysis. These findings suggest that IR-UWB radar could be a supportive tool for distinguishing high levels of mental stress, offering clinicians complementary diagnostic insights. Full article

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar. Full article