Wave Radar Research Articles

Significantly Enhanced Electromagnetic Wave Absorption Performances and Corresponding Mechanism of LaMn1-xFexO3 Perovskites

Improving the performance of LaMnO3 perovskite is essential to make it an efficient and ultra-light electromagnetic wave (EMW) absorbing material. In this work, LaMn1-xFexO3 powders (x = 0, 0.1, 0.2, 0.3, 0.4, 0.5) were synthesized by controlling the doping of Fe3+ ions at the B-site using the citric acid-assisted sol-gel method. The effects of Fe3+ ions doping content on crystal structures, magnetic properties and EMW absorption performances were investigated. The obtained LMFO-4 sample exhibited a minimal reflection loss of -54.99 dB at 13.68 GHz, with an adequate absorption bandwidth (RL < -10 dB) spanning 4.56 GHz (11.6∼16.16 GHz) at a matching layer thickness of 2.0 mm. The exceptional EMW absorption performances could be attributed to the Jahn-Teller effect, the double-exchange interactions and the magnetic interactions of Mn and Fe ions. Furthermore, the Computer Simulation Technology (CST) simulations demonstrated that the Radar Cross Section (RCS) value of LMFO-4 was lower than -25.10 dBm2 across angles ranging from -90° to 90° with a thickness of 2.0 mm, indicating the remarkable radar wave attenuation ability. These insights provided valuable guidance for advancing perovskite-based materials tailored for efficient and ultra-light electromagnetic wave absorption applications.

Drone Detection Using Dynamic-DBSCAN and Deep Learning in an Indoor Environment

Drones have found extensive utility in both public and personal places. Consequently, the accurate detection and tracking of drones have emerged as pivotal endeavors in terms of ensuring their optimal performance. This research paper introduces a novel application for discerning the movements of humans and drones from cloud points through the utilization of frequency-modulated continuous wave radar. The dynamic density-based spatial clustering of applications with noise (Dynamic-DBSCAN) algorithm was employed to classify cloud points into separate groups corresponding to the number of objects within the tracking area. Compared to the original DBSCAN algorithm, this method increased accuracy by about 16.8%, achieving an accuracy of up to 93.99%. Subsequently, a trio of deep learning algorithms— long short-term memory, deep neural network, and residual network (ResNet)—were harnessed to ascertain the categorization of each group as either human or drone. According to the results, ResNet achieved the best accuracy rate of 97.72%. Overall, this study underscores the efficacy of the proposed method in accurately and efficiently distinguishing between human and drone entities for effective monitoring and management.

A novel rehabilitation action recognition approach using AF-LiteFormer from millimeter wave radar

Abstract Rehabilitation action recognition is a hot research topic in the medical field, which serves as the foundation for achieving remote healthcare, unsupervised exercise, intelligent home healthcare, and possesses extensive application value. Currently, vision-based action recognition methods are susceptible to limitations imposed by factors such as range of motion and environmental lighting during human motion capture. Due to its ability to effectively protect patient privacy and its immunity to lighting conditions, this paper proposed a millimeter-wave radar-based rehabilitation action recognition system, AF-LiteFormer. Firstly, EfficientFormerV2 is employed as the baseline, then a Lite-MSLA-FFN block-and-layer is designed to replace MHSA in EfficientFormerV2, which improves the diversity of attention and reduces computational complexity. Meanwhile, a Lite-Subsample block-and-layer is designed to replace the dual-path downsampling part that composed of attention downsampling and stride attention to realize global modeling and multi-scale learning.&amp;#xD;Secondly, the iterative attention feature fusion (iAFF) mechanism is introduced to improve the recognition accuracy of rehabilitation actions. Finally, the effectiveness of the AF-LiteFormer model is validated on a self-collected rehabilitation action dataset and a publicly available micro-Doppler dataset. Experimental results show that the overall performance of the AF-LiteFormer model is better than the State-of-the-Art model (SOTA), the recognition accuracy of rehabilitation actions is as high as 99.7%, and it has strong generalization ability.&amp;#xD;

Long-term tracking for high-frequency surface wave radar based on heading constraint filtering combined ELM

Abstract Aiming at the problem of low measurement resolution and poor tracking accuracy in High-frequency surface wave radar (HFSWR) tracking field, a tracking method that combines the heading constraint filter and Extreme Learning Machine (ELM) is proposed. Compared with the existing research, the innovation of this study lies in proposing a two-stage tracking method based on the short-term stable state of the target, and similarity of long-term trajectory. Firstly, the heading constraint information is introduced into the estimator and improve the estimation accuracy. In multi-target tracking scenarios, many tracklets can be obtained. Then, the ELM learns robust features of tracklets to achieve the trajectory segment classification from the same target. This method can be widely applied to long-term tracking of cargo or passenger ships with fixed destinations, significantly improving tracking performance by utilizing implicit domain knowledge without additional information. The actual data of this paper comes from HFSWR on Bohai Bay. Both the simulation and actual measurement experiments show that the proposed cascaded tracking method achieves long-term continuous tracking, and generates more complete trajectories. Moreover, the proposed Extended Kalman filter based on pseudo-measurement and intercept parameters (IPM-EKF) exhibits better tracking stability and accuracy in limited scan steps. This study provides new ideas and methods for improving the tracking performance of special targets in the HFSWR field by utilizing motion features.

Three-Dimensionally Printed K-Band Radar Stealth Lightweight Material with Lotus Leaf Structure.

K-band radar waves have high penetration and low attenuation coefficients. However, the wavelength of this radar wave is relatively short; thus, designing and preparing both broadband and wide-angle radar wave absorbers in this band presents considerable challenges. In this study, a resin-based K-band radar wave absorber with a biomimetic lotus leaf structure was designed and formed by UV curing. Here, microscale lotus leaf papillae and antireflection structures were prepared using a DLP 3D printer, and the contact angle between the material and water droplets was increased from 56° to 130°. In addition, the influence of the geometric parameters of the lotus leaf antireflection structure on the electromagnetic absorption performance and mechanical strength was investigated. After simulation optimization, the maximum electromagnetic loss of the lotus leaf structure 3D-printed sample was -32.3 dB, and the electromagnetic loss was below -10 dB in the 20.8-26.5 GHz frequency range. When the radar incidence angle was 60°, the maximum electromagnetic loss was still less than -10 dB. The designed lotus leaf structure has a higher mechanical energy absorption per unit volume (337.22 KJ/m3) and per unit mass (0.55 KJ/Kg) than commonly used honeycomb lightweight structures during the elastic deformation stage, and we expect that the designed structure can be used as an effective lightweight material for K-band radar stealth.

Micro‐motion parameters extraction of rotating target based on analytical solution in vortex electromagnetic wave radar

AbstractThe linear Doppler effect and angular Doppler effect in vortex electromagnetic wave radar echoes can be used to extract comprehensive micro‐motion parameters. However, the analytical solution of micro‐motion parameters for rotating targets in vortex EM wave radar has not been obtained. To solve the above problems, the analytical solution of micro‐motion parameters is derived, and a micro‐motion parameter extraction method is proposed. Firstly, the shape and properties of the angular Doppler curve in various cases are discussed. Subsequently, the properties of three points on the angular Doppler curve are derived. The micro‐motion parameters are obtained based on the three points. The simulations demonstrate that the proposed method can greatly reduce the computational complexity and improve the accuracy of micro‐motion parameter extraction.

High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.

Diffraction Separation for the Ground Penetrating Radar Data by Masking Filters

Ground penetrating radar is a high-resolution, efficient, non-destructive geophysical detection method. It is widely used in various application scenarios such as tunnel geological prediction and road maintenance. Ground penetrating radar data contains a variety of valid signals as well as noise. The diffracted waves of ground penetrating radar contain high-resolution small target imaging information. A critical challenge in GPR applications is how to extract diffracted waves from the wave fields. We provide a strategy to achieve this goal by applying the masking filters. Considering the complexity of the ground penetrating radar wave field and the weak energy of the diffracted waves, the median filter is first employed to suppress the linear reflections and then the f-k filter and filter are implemented to further increase the proportion of diffractions in the wave fields. Three numerical experiments are employed to test the diffraction-separation method.

Superluminal propagation of light in a atomic medium

We manipulated and adjusted the absorption, dispersion, group index, and group velocity in a sodium atomic medium by controlling the strength and collective phase of the driving fields. The group index in these regions is calculated to be [Formula: see text]250,000. The group velocity [Formula: see text] in these locations is measured to be [Formula: see text][Formula: see text]m/s. An investigation is conducted on a negative delay of [Formula: see text]s in the sodium medium. The presence of negative delay and negative group velocity indicates the occurrence of superluminal propagation of light pulses within the medium. The negative group index is increased from [Formula: see text] to [Formula: see text], and the negative delay time is increased from [Formula: see text]s to [Formula: see text]s, with the driving field [Formula: see text] having a Rabi frequency. The work presented in this paper has substantial implications for the fields of gravitational wave detection, radar technology, and temporal cloaks.

An IoT-based contactless neonatal respiratory monitoring system for neonatal care assistance in postpartum center

According to previous studies, one of the major causes of 20 % to 25 % of neonatal deaths is respiratory distress syndrome (RDS). Early identification, progressive monitoring, and treatment and/or management of neonatal RDS can substantially increase the rate of survival in neonates. However, global research indicates frequent shortages and burnout among nursing staff, especially in postpartum units, contributing to the difficulty in early identification of RDS in neonates. Clinicians currently use breathing sounds and frequency as key criteria in the Neonatal Resuscitation Program (NRP) for identifying and treating RDS. In practice, the monitoring of respiratory signal abnormalities relies on sensor patches, which frequently detach from the neonates’ slippery skin, leading to potential skin injuries and unstable signal reception. This paper presents an Internet of Things (IoT)-based contactless neonatal respiratory monitoring system that integrates computer vision (CV), beamforming microphone array (BFMA), and millimeter Wave (mmWave) radar, all connected to a cloud platform. Clinical trials revealed that CV-based neonatal feature identification achieved over 96 % accuracy within 40 cm to 120 cm. The neonatal breathing sound strengthening, utilized CV and BFMA, achieved an average sound-to-noise ratio (SNR) of 5.07 dB, and CV with mmWave radar reduced chest displacement signal error from 0.66 to 0.26 BPM. Additionally, survey results showed that doctors and clinical personnel were satisfied with the system's functionality and usability. This demonstrates the system's ability to assist in monitoring respiratory signals of swaddled neonates and in the early identification of neonatal RDS, with further applications in neonatal care at postpartum centers.

Noncontact Soil Moisture Estimation Using Continuous Wave Radar and Deep Learning

Noncontact Soil Moisture Estimation Using Continuous Wave Radar and Deep Learning

Coconut shell waste-based ZnFe2O4/PANI/rGO nanohybrid composites as excellent radar-absorbing material

In this work, we developed ZnFe2O4/PANI/rGO nanocomposites based on coconut shell waste through in situ polymerization as excellent radar-absorbing material (RAM). This research mainly focused on adjusting the ZnFe2O4 content to obtain optimal absorption of radar waves, especially in the X-band. The results showed that the diffraction pattern of the ZnFe2O4/PANI/rGO nanocomposites showed multiple phases (crystalline and amorphous), which increased the ZnFe2O4 content and affected the increasing diffraction intensity. The functional group of the ZnFe2O4/PANI/rGO nanocomposites was identified with the presence of the M–O group originating from ZnFe2O4 at wavenumbers 517 and 419 cm−1 of Zn–O and Fe–O groups, respectively. Meanwhile, quinoid and benzenoid rings from PANI and CO from rGO were identified at wavenumbers 1580, 1504, and 1710 cm−1, respectively. The morphology of the ZnFe2O4/PANI/rGO nanocomposites showed PANI was distributed on the ZnFe2O4 nanoparticles, which were subsequently deposited on the rGO. The ZnFe2O4/PANI/rGO nanocomposites exhibited superparamagnetic properties, as confirmed by their coercivity values ranging from 9.2 to 124.4 Oe. Interestingly, the ZnFe2O4/PANI/rGO nanocomposites with the P3 sample showed optimal RAM performance with a minimum reflection loss value of −39.8 dB at 7.6-GHz frequency with an effective absorption bandwidth of 1.1 GHz.

Rotor unmanned aerial vehicle localization in the building sheltered area based on millimetre‐wave frequency‐modulated continuous wave radar

AbstractRotor unmanned aerial vehicles (UAVs) play an important role in both military and civilian fields nowadays. The safety risks associated with the UAVs increase the urgent need for detecting UAVs in urban environments as well. Moreover, UAVs are easily located in the non‐line‐of‐sight (NLOS) building sheltered area relative to the radar, making it very challenging to detect and localise. A novel algorithm for localising the rotor UAV in the common L‐shaped street building sheltered area is proposed. First, the authors establish a multipath signal model for a rotor UAV hovering over an L‐shaped street scene, leveraging the frequency‐modulated continuous wave signal. Then, the multipath information of the UAV is extracted by identifying the rotating periodicity of the blade embedded in the time‐frequency spectrum of the received signal. The back projection imaging is then conducted on the UAV‐related multipath. After extracting multipath ghosts in the image, the street area, where the UAV locates, can be determined, and the UAV is further localised using the path reflection characteristics of this area. Simulations and practical experiments based on millimetre waves indicate that the proposed method can enable high‐accuracy estimation of rotor UAV in the NLOS building sheltered area.

Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR

The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method.

Heterostructure engineering of N-doped Co@ carbon nanotubes toward broadband efficient electromagnetic absorption

The synergistic effect of magnetic loss and dielectric loss, as well as the reasonable construction of microstructure, have a significant effect on improving the absorption performance of electromagnetic wave absorbers. In this paper, N-doping Co@ carbon nanotubes (NCC) composites are successfully grown in situ on cobalt particles using a cobalt source as a catalyst. The metal particle Co is encapsulated within the carbon nanotubes and N is doped in the carbon lattice. This special structure introduces a large number of heterogeneous interfaces and defects, leading to optimized impedance matching and strong electromagnetic attenuation. The results showed that the minimum reflection loss is −69.33 dB for NCC800-1 with a thickness of 1.6 mm, and NCC900-4 possesses the widest effective absorption bandwidth of 7.70 GHz at a thickness of 2.1 mm with other NCC composites. Furthermore, the radar scattering cross section simulation results confirm that the prepared absorbing coatings can well suppress radar waves in different directions. The outstanding absorption performance is attributed to the defects and heterogeneous interfaces in the cobalt-catalyzed carbon nanofibers, the conductive network formed by carbon fibers, and the synergistic effects of magnetic and dielectric losses. This work presents a novel engineering approach for synthesizing absorbers with multiple loss mechanisms and heterogeneous structures, offering a promising pathway for the development of high-performance electromagnetic wave absorbing materials.

Heading estimation algorithm for pedestrian navigation using a dual-foot-mounted IMU with a millimetre wave radar

Heading estimation is crucial in pedestrian navigation systems based on IMUs, but accuracy often suffers degradation due to error accumulation. This study introduces a novel heading estimation algorithm using dual-foot-mounted IMUs with a millimeter-wave radar to enhance accuracy. The algorithm includes a Single-foot Multiconditional Constraint Algorithm (SMC-A), adaptive step length constraint, and bipedal maximum heading difference constraint. Firstly, the SMC-A utilizes zero speed correction, zero angular rate updates, and self-observing heading error corrections through extended Kalman filtering. Then, the adaptive step length constraint limits pedestrian motion, and the bipedal constraint corrects heading errors. Finally, the Bipedal Multiconditional Constraint Algorithm (BMC-A) incorporates millimeter-wave radar measurements of foot distance. Experiments show heading deviations of 3.74° and 4.03° are obtained for the left and right feet over a 30-minute, 1.45 km trial. The new algorithm improves heading accuracy by 68.19 % and 70.67 % for the left and right feet, respectively, compared to traditional SMC-A.

MRI retrospective respiratory gating and cardiac sensing by CW Doppler radar: A feasibility study.

This study investigates the feasibility of non-contact retrospective respiratory gating and cardiac sensing using continuous wave Doppler radar deployed in an MRI system. The proposed technique can complement existing sensors which are difficult to apply for certain patient populations. We leverage a software-defined radio for continuous wave radar at 2.4 GHz to detect in-vivo respiratory and cardiac timescrolled signals. In-bore radar signal demodulation is verified with full electromagnetic simulations, and its functionality is validated on a test bench and within the MR bore with four normal subjects. Radar sensing was compared against well-known references: electrocardiography on a test bench, system bellows, and pulsed plethysmography sensors with in the MRI bore. The feasibility of noncontact cardiac rate sensing, dynamic breathing sequence synchronization, and in-bore motion correction for retrospective respiratory gating applications was demonstrated. Optimal radar front-end system arrangement, along with spectral isolation and narrow bandwidth of operation, enable MRI-compatible and interference-free motion sensing. The signal-to-noise-ratio degradation by the radar integration was within 4.5% on phantom images. We confirmed that in-bore retrospective motion correction using CW Doppler radar is feasible without MRI system constraints. Non-contact motion correction sensing in MRI may provide better patient handling and through put by complementing existing system sensors and motion correction algorithms.

Extraction of Weak Harmonic Target Signal from Ionospheric Noise of High Frequency Surface Wave Radar

Extraction of Weak Harmonic Target Signal from Ionospheric Noise of High Frequency Surface Wave Radar

SRCNN: Stacked-Residual Convolutional Neural Network for Improving Human Activity Classification Based on Micro-Doppler Signatures of FMCW Radar

Current methods for daily human activity classification primarily rely on optical images from cameras or wearable sensors. Despite their high detection reliability, camera-based approaches suffer from several drawbacks, such as low-light conditions, limited range, and privacy concerns. To address these limitations, this article proposes the use of a frequency-modulated continuous wave radar sensor for activity recognition. A stacked-residual convolutional neural network (SRCNN) is introduced to classify daily human activities based on the micro-Doppler features of returned radar signals. The model employs a two-layer stacked-residual structure to reuse former features, thereby improving the classification accuracy. The model is fine-tuned with different hyperparameters to find a trade-off between classification accuracy and inference time. Evaluations are conducted through training and testing on both simulated and measured datasets. As a result, the SRCNN model with six stacked-residual blocks and 64 filters achieves the best performance, with accuracies exceeding 95% and 99% at 0 dB and 10 dB, respectively. Remarkably, the proposed model outperforms several state-of-the-art CNN models in terms of classification accuracy and execution time on the same datasets.

Lineaments Extraction and Analysis Using Sentinel 2B, Sentinel 1A Data and ALOS PALSAR DEM: A Case Study of the Igoudrane Region, Jbel Saghro, Eastern Anti-Atlas, Morocco

The remote sensing techniques have been used several years ago in many geological studies, and have proved their utility especially in lineament extraction. During this research, we extracted the lineaments automatically as well as manually in the Igoudrane region, Imiter inlier, eastern Anti-Atlas. The purpose of this research is to extract and map lineaments in the study area, and then compare the results provided, where the VNIR bands of Sentinel-2B, Sentinel-1A and the ALOS PALSAR DEM (12.5 m) were used. The directional filters were applied to the VNIR bands of Sentinel-2B into six directions, in the interest of attaining a significant lineament contrast. The VH and VV radar data provided the greatest number of lineaments (7243) by LINE module algorithm, while the shaded relief and slope were applied to the ALOS PALSAR DEM, which gave a higher number of lineaments than the VNIR bands of Sentinel-2B (4699, 4647 respectively). The manual extraction on the VV and VH polarizations yielded 441 lineaments, while the VNIR bands of Sentinel-2B provided 453 lineaments. Furthermore, the main orientations of the lineaments are NE-SW, ENE-WSW and NNE-SSW. The lineaments extracted from the VNIR bands (Sentinel-2B) and ALOS PALSAR’s DEM exhibited an optimal match along with the fractures digitized manually and from the geological maps of the research area. The results revealed that Sentinel-1 data is less efficient than ALOS PALSAR DEM data, especially in arid regions where the capacity of DEM radar waves to penetrate rises for thinner sedimentary layers, offering deeper information.