Radar Detection Research Articles

On the Detection of Vulnerable Road Users—Simulating Limits of an FMCW Radar

The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp’s accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments.

Three-dimensional orthorectified simulation and ground penetrating radar detection of interlayer bonding condition in asphalt pavements

Three-dimensional orthorectified simulation and ground penetrating radar detection of interlayer bonding condition in asphalt pavements

Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery

In the passive bistatic radar (PBR) system,methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.

Detection Performance Analysis of Marine Wind by Lidar and Radar under All-Weather Conditions

Accurate marine wind detection under all-weather conditions is crucial for maritime activities. The joint detection of lidar and radar is supposed to be a potential way to carry out the all-weather sensing of wind. However, their performance analysis has not been well studied, particularly in the far sea area, where the wind-tracing particles are quite different from those inland. Based on the particle distributions above the sea surface under different weather conditions, this study investigated the scattering and attenuation effects of lidar and radar waves in open sea areas with the Mie theory and T-matrix method. Then, the maximum detection range and velocity accuracies of lidar/radar were comprehensively analyzed based on detection principles to optimize the combination of lidar and radar. According to the simulation results, it was difficult to maintain the detection capability of a single lidar/radar under all-weather conditions, and 1.55 μm lidar and W-band radar presented a promising joint detection scheme, as they exhibited optimal weather adaptability in clear sky and precipitation conditions, respectively.

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

Traditional radar target detectors, which are model-driven, often suffer remarkable performance degradation in complex clutter environments due to the weakness in modeling the unpredictable clutter. Deep learning (DL) methods, which are data-driven, have been introduced into the field of radar target detection (RTD) since their intrinsic non-linear feature extraction ability can enhance the separability between targets and the clutter. However, existing DL-based detectors are unattractive since they require a large amount of independent and identically distributed (i.i.d.) training samples of target tasks and fail to be generalized to the other new tasks. Given this issue, incorporating the strategy of meta-learning, we reformulate the RTD task as a few-shot classification problem and develop the Inter-frame Contrastive Learning-Based Meta Detector (IfCMD) to generalize to the new task efficiently with only a few samples. Moreover, to further separate targets from the clutter, we equip our model with Siamese architecture and introduce the supervised contrastive loss into the proposed model to explore hard negative samples, which have the targets overwhelmed by the clutter in the Doppler domain. Experimental results on simulated data demonstrate competitive detection performance for moving targets and superior generalization ability for new tasks of the proposed method.

Polarization-insensitive, wideband reflective metasurface generating orbital angular momentum vortex beam

In this paper, a low-profile polarization-insensitive reflective metasurface is proposed to generate high gain and wideband orbital angular momentum (OAM) in X-band (in the frequency range of 8−12GHz). By adjusting a subwavelength pseudo-screw-shaped meta-atom’s size the required phase compensation has been achieved for designing a reflectarray antenna at 10GHz. The theoretical analyses and simulation results confirm that the proposed structure can effectively generate the first-, second-, third-, and fourth-order OAM vortex beams at the operating frequency. To verify the bandwidth of the suggested metasurface, the structure is implemented for first-order OAM mode generation at various frequencies. The outcomes of investigations show the OAM operational bandwidth and 1.5-dB gain bandwidth are attained to be 40 %, and 27.5 %, respectively. Meanwhile, approximately 14.35 % reflection efficiency was procured for the overall design at the operating frequency of 10GHz. The numerical aperture of the reflectarray metasurface is also conceptually defined and calculated for varying positions of the center-feed antenna. The admitted performance and substantial characteristics of the proposed OAM reflectarray antenna may make it a useful candidate for radar imaging and detection applications as well as broadband microwave communications.

Multistatic Passive Radar for drone detection based Random Finite State

Considering the implication of radar sensors in our daily life and environment. Localizing and identifying drones are becoming a research with a greater focus in recent years. Consequently, when an unmanned aerial vehicle is used with bad intention, this can lead to a serious public safety and privacy probem. This study investigate pratical use of spectrum range for multistatic passive radar (MSPR) signal processing . Firstly, signal processing is performed after MSPR sensing detection range ,this include multipath energy detection, reference signal extraction, and receiving antenna configuration. Secondly, based on the MSPR nature, the reference signal is extracted and analyzed. In addition,taking into consideration the vulnerability of passive radar when comes to a moving target localization and real time tracking detection ,a novel method for spectrum sensing and detection which relies on Gaussian filter is proposed. The main goal is to optimize the use of the reference signal extracted with minimum interference as the shared reference signal in spectrum sensing. This will improve the system detection capability and spectrum access. Finally, a recursive method based on Bernoulli random filter is proposed, this takes consideration of drone’s present and unknown states based on time. Moreover, a system is developed meticulously to track and enhance detection of the target. A careful result of the experiment demonstrated that spectral detection can be achieved accurately even when the drone is moving while chasing its position. It shows that Cramer Rao lower error bounds remains significantly within 3% range.

Synergistic enhancing effect for electromagnetic wave absorption via melamine resin-based magnetic core-shell microspheres/multi-walled carbon nanotubes

Rational design of corrosion-resistant electromagnetic wave (EMW) absorbing materials with wide bandwidth, strong absorption, and low filling ratio remains a significant challenge. In this work, the melamine resin-based magnetic core-shell microspheres (MF@Fe3O4@SiO2) were synthesized via dispersion polymerization and in-situ growth method. Experimental and simulation results indicated that the content and magnetism of the magnetic shell coordinated with the MF-core could be tactfully regulated with a small amount of incorporated Ni content. Moreover, the dispersion and corrosion resistance of MF@Fe3O4 core-shell microspheres were obviously enhanced by coating SiO2. It is worth noting that after the composite of MF@Fe3O4@SiO2 with MWCNTs-20, the presence of numerous heterogeneous interfaces, the tactfully improved conductive loss, and the optimized impedance matching synergistically result in superior EMW absorption properties. When the filling amount was 30 wt%, F9N1-S/MWCNTs-20 with a matching thickness of 1.76 mm, exhibited the maximum effective absorption bandwidth (EABmax) of 6.55 GHz, while maintaining a reflection loss (RL) of −62.94 dB at 1.53 mm. When the thickness of F9N1-S/MWCNTs-30 was increased to 3.24 mm, the RLmin at low frequencies was achieved at −67.14 dB. Besides, the simulation of radar cross section values ascertains the enormous potential of F9N1-S/MWCNTs to achieve stealth under radar detection. This study introduces a novel method for producing lightweight and efficient EMW absorbers with corrosion resistance.

Detection and validation of common noctule bats (Nyctalus noctula) with a pulse radar and acoustic monitoring in the proximity of an onshore wind turbine.

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine's nacelle. The radar's detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife.

Additively-Manufactured Broadband Metamaterial-Based Luneburg Lens for Flexible Beam Scanning

Multi-beam microwave antennas have attracted enormous attention owing to their wide range of applications in communication systems. Here, we propose a broadband metamaterial-based multi-beam Luneburg lens-antenna with low polarization sensitivity. The lens is constructed from additively manufactured spherical layers, where the effective permittivity of the constituting elements is obtained by adjusting the ratio of dielectric material to air. Flexible microstrip patch antennas operating at different frequencies are used as primary feeds illuminating the lens to validate the radiation features of the lens-antenna system. The proposed Luneburg lens-antenna achieves ±72° beam scanning angle over a broad frequency range spanning from 2 GHz to 8 GHz and presents a gain between 15.3 dBi and 22 dBi, suggesting potential applications in microwave- and millimeter-wave mobile communications, radar detection and remote sensing.

X-Band Radar Detection of Small Garbage Islands in Different Sea State Conditions

This paper presents an assessment of X-band radar’s detection capability to monitor Small Garbage Islands (SGIs), i.e., floating aggregations of marine litter consisting chiefly of plastic, under changing sea states. For this purpose, two radar measurement campaigns were carried out with controlled releases at sea of SGI modules assembled in the laboratory. One campaign was carried out with a calm sea and almost no wind in order to determine the X-band radar system’s detection capabilities in an ideal scenario, while the other campaign took place with rough seas and wind. An analysis of the data acquired during the campaigns confirmed that X-band radar can detect small aggregations of litter floating on the sea surface. To demonstrate the radar’s ability to detect SGIs, a statistical analysis was carried out to calculate the probability of false alarm and the probability of detection for two releases at two different distances from the radar. For greater readability of this work, all of the results obtained are presented both in terms of radar intensity and in terms of the radar cross-section relating to both the targets and the clutter. Another interesting study that is presented in this article concerns the measurement of the speed of movement (drift) of the SGIs compared with the measurement of the speed of the surface currents provided at the same time by the radar. The study also identified the radar detection limits depending on the sea state and the target distance from the antenna.

Preparation of Fibrous Three-Dimensional Porous Materials and Their Research Progress in the Field of Stealth Protection.

Intelligent and diversified development of modern detection technology greatly affects the battlefield survivability of military targets, especially infrared, acoustic wave, and radar detection expose targets by capturing their unavoidable infrared radiation, acoustic wave, and electromagnetic wave information, greatly affecting their battlefield survival and penetration capabilities. Therefore, there is an urgent need to develop stealth-protective materials that can suppress infrared radiation, reduce acoustic characteristics, and weaken electromagnetic signals. Fibrous three-dimensional porous materials, with their high porosity, excellent structural adjustability, and superior mechanical properties, possess strong potential for development in the field of stealth protection. This article introduced and reviewed the characteristics and development process of fibrous three-dimensional porous materials at both the micrometer and nanometer scales. Then, the process and characteristics of preparing fibrous three-dimensional porous materials through vacuum forming, gel solidification, freeze-casting, and impregnation stacking methods were analyzed and discussed. Meanwhile, their current application status in infrared, acoustic wave, and radar stealth fields was summarized and their existing problems and development trends in these areas from the perspectives of preparation processes and applicability were analyzed. Finally, several prospects for the current challenges faced by fibrous three-dimensional porous materials were proposed as follows: functionally modifying fibers to enhance their applicability through self-cross-linking; establishing theoretical models for the transmission of thermal energy, acoustic waves, and electromagnetic waves within fibrous porous materials; constructing fibrous porous materials resistant to impact, shear, and fracture to meet the needs of practical applications; developing multifunctional stealth fibrous porous materials to confer full-spectrum broadband stealth capability; and exploring the relationship between material size and mechanical properties as a basis for preparing large-scale samples that meet the application's requirement. This review is very timely and aims to focus researchers' attention on the importance and research progress of fibrous porous materials in the field of stealth protection, so as to solve the problems and challenges of fibrous porous materials in the field of stealth protection and to promote the further innovation of fibrous porous materials in terms of structure and function.

Adapting Object Detection Models for Multi-Target Detection Utilizing Radars

This paper investigates the use of Deep Learning (DL) in multiple input multiple output (MIMO) radar target detection, focusing on azimuth and elevation estimation. Traditional methods face challenges like interference and reflections, especially in multi-target scenarios. Feature extraction conventionally relies on range correlation, Doppler filtering, and angle beamforming, followed by detection after constant false alarm rate (CFAR) processing. However, early data sparsification by bin selection often leads to information loss, particularly with large data cubes required for practical implementation. Deep Learning techniques offer an alternative, specifically in azimuth and elevation detection at earlier stages of radar data processing. We developed a convolutional neural network (CNN) model that achieved Mean Square Errors (MSE) of 0.149 for azimuth and 0.168 for elevation on single-target data from 5,000 samples. The model's performance in dual-target scenarios showed MSEs ranging from 0.838 to 1.845, tested on 8,000 samples from a dataset of 72,000. This paper details the model development process, its impact on radar target detection, and potential future research directions involving the substitution of multi-bin Deep Learning blocks with traditional methods.

GDB‐YOLOv5s: Improved YOLO‐based model for ship detection in SAR images

AbstractIn recent years, deep learning methods were good solutions for object detection in synthetic aperture radar (SAR) images. However, the problems of complex scenarios, large object scale differences and imperfect fine‐grained classification in ship detection were intractable. In response, an improved model GDB‐YOLOv5s (Improved YOLOv5s model incorporating global attention mechanism (GAM), DCN‐v2 and BiFusion) is designed. This model introduces deformable convolution networks (DCN‐v2) into the Backbone to enhance the adaptability of the receptive field. It replaces the original Neck's PANet structure with a BiFusion structure to better fuse the extracted multiscale features. Additionally, it integrates GAM into the network to reduce information loss and improve global feature interaction. Experiments were conducted on single‐class dataset SSDD and multi‐class dataset SRSSD‐V1.0. The results show that the GDB‐YOLOv5s model improves mean average precision scores (mAP) significantly, outperforming the original YOLOv5s model and other traditional methods. GDB‐YOLOv5s also improves Precision‐score (P) and Recall‐score (R) for fine‐grained classification to some extent, thereby reducing false alarms and missed detections. It has been proved that the improved model is relatively effective.

High-precision imaging of small voids in tunnel lining based on reverse time migration

Abstract Diseases such as voids behind the initial support of the tunnel will lead to problems such as lining rupture and concrete damage in the tunnel structure, which seriously affects the driving safety in the tunnel. In tunnel construction, ground penetrating radar is frequently employed as a method for detecting hidden defects. However, when the cavity size is small, there will be a large error when the original radar data is directly interpreted, which cannot meet the needs of practical engineering. To enhance the precision in tunnel detection, the Finite-Difference Time-Domain method is used to simulate various small cavities of different shapes located behind the primary support lining of the tunnel, and the study involves examining the electromagnetic response characteristics of different kinds of holes. The migration techniques of KIRCHHOFF, F-K, and reverse time are employed to reconstruct the hole target. Furthermore, digital morphology is employed to enhance the clarity of the image. Finally, the edge detection technology is used to further extract the hole features. After that, small cavities of different shapes are buried in the established outdoor concrete model box and radar detection is carried out. The detection position of the reconstructed radar image and the actual position of the cavity in the model box are verified. The results show that compared with KIRCHHOFF migration and F-K migration, the image processed by reverse time migration is clearer and more intuitive, and can restore the contour of small holes well. The research findings can serve as a reference for the interpretation of radar data of the cavity behind the initial support of the tunnel.

NRENet: Neighborhood removal-and-emphasis network for ship detection in SAR Images

In recent years, object detection in Synthetic-Aperture Radar (SAR) images still faces many challenges, especially for ship detection. Small or dense ships are vulnerable to the interference of complex scenes such as ports and land. In feature extraction, a large amount of redundant information on the feature map will further reduce the network’s attention to small-sized ships. Therefore, in this paper, we propose a network called neighborhood removal-and-emphasis network (NRE-Net), including an object neighborhood removal (ONR) strategy and a neighborhood feature emphasis (NFE) module. Among them, the ONR strategy directly removes the complex background information around the ship before feature extraction, only retaining effective contextual neighborhoods conducive to ship detection and avoiding the interference of complex background information on the network. The NFE module is based on ONR to extract features and form a weight map of small-sized ships or complex images. This module can adaptively recognize the detection neighborhood of each ship and highlight the detection ship on the feature map. Our network has validated the effectiveness of the method on multiple SAR ship datasets and improved the object AP for each size. Our code is available at: https://github.com/Xidian-AIGroup190726/NRENet.

Research on Response Characteristics of GPR Method for Detecting Phreatic Lines in Embankments

The excessive elevation of the phreatic lines under continuous high-water levels is the cause of many dangerous situations in embankments. Therefore, accurately understanding the dynamic changes in the infiltration characteristics of embankments is of great significance for judging and evaluating the stability of engineering. This study conducted indoor model experiments and used ground penetrating radar to detect the phreatic lines of soil. By comparing the radar detection results with the measured position of the phreatic lines, it was determined that the phreatic lines appeared as a continuous strong reflection surface with similar shapes in the radargram, and the reflection below the reflection surface became weaker. Through time-domain analysis of the single waveform at different time points, it was found that the amplitude of the reflected wave at the saturation line was larger, and the energy attenuation below the saturation line was faster, resulting in a rapid decrease in amplitude; perform Fourier transform on a single channel signal to obtain a spectrogram, and perform frequency domain analysis on it. The main frequency is around 600MHz, and the second main frequency is around 900MHz. As the saturation line rises, the main frequency gradually shifts towards lower frequencies.

Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms

Abstract. Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications.

A Sliding Window Technique-Based Radar and Camera Fusion Model for Object Detection in Adverse Weather Condition

A Sliding Window Technique-Based Radar and Camera Fusion Model for Object Detection in Adverse Weather Condition

CenRadfusion: fusing image center detection and millimeter wave radar for 3D object detection

CenRadfusion: fusing image center detection and millimeter wave radar for 3D object detection