Radar Performance Research Articles

Wave transmittance performance prediction of curved radome prefabricated with continuous fibers plain weave fabric

AbstractThe plain weave fabric made of special fibers was characterized by low density, lightness, ease of co‐planarity, and strong designability, widely used in the production of radar covers, stealth fighter skin, and shielding walls, among other complex electromagnetic shielding components. However, high‐performance fibers, influenced by weaving processes and the interlacing patterns of yarns, possess significant flexibility. Under external loads, fibers are prone to relative slippage, leading to uncertain geometric distortions. Furthermore, changes in their electromagnetic wave transmission properties make it difficult to accurately predict the electromagnetic performance of fiber radar covers. This study primarily focuses on the macro deformation behavior of plain weave fabrics, establishes the geometric mapping relationship for covering curved surface radar covers using plain weave fabrics, and ultimately develops a predictive model for the electromagnetic wave transmittance performance of plain weave fabrics. The research examines the impact of macro and micro geometric structural parameters of plain weave fabrics on electromagnetic wave transmission properties, ultimately enabling quantitative analysis of the electromagnetic wave transmission properties of woven radar covers. The conclusions indicated that changes in the plain weave fabric's pitch directly affect the fiber volume fraction, thereby affecting the transmittance. The greater the shear deformation of plain weave fabric laid on a curved surface, the thicker the fabric and the higher the fiber volume fraction. At the higher curvature ends of curved surfaces, the transmittance was lower than at the lower curvature base.Highlights Explained the deformation behavior of plain weave fabric. A wave transmission model for radome made of plain weave fabric was established. The influence mechanism of fabric deformation on wave transmission was revealed.

Ionospheric clutter reduction for high‐frequency surface‐wave radar based on time‐frequency‐ridgelet filtering

AbstractThe existence of ionosphere clutter, the target limits the performance of high frequency surface wave radar (HFSWR). It is more desirable to solve the problem of ionosphere clutter mitigation. Existing methods suffer difficulty when the clutter is nonstationary. To overcome this issue, a novel ionospheric clutter mitigation algorithm based on Time‐Frequency‐Ridgelet Filtering (TFRF) is proposed in this paper. Our study shows that the time‐frequency ridgelet coefficients of the clutter are nonstationary, whereas the targets' are stationary. Therefore, the ionosphere clutter can be reduced by the TFRF method. Experimental results show that the proposed TFRF method can effectively mitigate the nonstationary ionospheric clutter, and can improve the Signal to Clutter and Noise Ratio and the detection performance of HFSWR.

A Parameter Estimation-Based Anti-Deception Jamming Method for RIS-Aided Single-Station Radar

Multi-station radar can provide better performance against deception jamming, but the harsh detection requirements and risk of network destruction undermine the practicability of the multi-station radar. Therefore, it is necessary to further explore the anti-deception jamming performance of a single-station radar. This paper introduces a novel method, based on parameter estimation with a virtual multi-station system, to discriminate range deceptive jamming. The system consists of a single-station radar assisted by the reconfigurable intelligent surfaces (RIS). A unified parameter estimation model for true and false targets is established, and the convex optimization method is applied to estimate the target location and deception range. The Cramer–Rao lower bound (CRLB) of the target localization and the measured deception range is then derived. By using the measured deception range and its CRLB, an optimal discrimination algorithm in accordance with the Neyman–Pearson lemma is designed. Simulation results demonstrate the feasibility of the proposed method and analyze the effects of factors such as signal-to-noise ratio (SNR), deception range, jammer location, and the RISs station arrangement on the discrimination performance.

A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.

A review of research on optical true time delay technology

Light controlled phased array has the advantages of fast response speed, compact system, diverse functions, and flexible control, and has been widely applied in many scientific and technological fields. Optical true delay technology (OTTD) is the most direct technical means to achieve phase delay of optical carrier signals, and it is also the most basic technical means to implement optical controlled beamforming systems. In order to fully understand the optical true delay technology, this article first elaborates on the principle of phased array antennas and the reasons for beam strabismus, and analyzes the impact of true delay on the performance of phased array radar. Then, the basic principle, technological progress, and related applications of optical true delay are introduced. Taking four common structures of optical true delay lines as examples,which are micro-ring resonant cavity array, grating ture time delay line, multi-path switchable optical ture time delay line（OTTDL）, and wavelength selective optical delay line, their performance in delay accuracy, adjustable delay range, and frequency bandwidth are compared. Finally, the current problems and future development trends of optical controlled beamforming technology were summarized.

Design and study of electromagnetic wave absorbing structure of multilayer GN/GF/EP composites

With the improvement of radar absorption performance requirements, the research of absorbing materials is an important research direction. In this paper, a graphene/glass fiber/epoxy (GN/GF/EP) structure design scheme is proposed. By measuring the electromagnetic parameters, the influence of different GN content on the absorber performance is analyzed, and the bilayer absorber material combination with the best absorber performance is determined. The double layer absorbing material was made of GN/GF/EP-1.0% (thickness 0.4 mm) and GN/GF/EP-2.5% (thickness 1.7 mm). The peak reflection loss of the double layer absorbing material is −41.55 dB, the center frequency is 10.93 GHz, and the effective absorption bandwidth is 2.86 GHz (9.34∼11.20 GHz).

Reconfigurable Intelligent Surfaces Assisted NLOS Radar Anti Jamming Using Deep Reinforcement Learning

The complexity of the radar environment increases with technological advancement, especially when considering the difficulties presented by repeating jammers. These jammers can impede radar detection, especially when they create false targets in non-line-of-sight (NLOS) situations. This study focuses on optimizing the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to address the problem of NLOS between a target and radar for detection in order to address these NLOS issues. Specifically, we investigate RIS phase shift optimization using a Genetic Algorithm (GA) to address the challenges posed by repeating jammers across various dynamic scenarios. Our objective is to increase the radar system’s ability to detect actual targets in non-LOS scenarios when repeater jammers are present in the environment. According to the experimental results, this method offers a practical way to mitigate the effects of repeater jammers by improving radar detection performance in NLOS environments.

The improvement of radar absorption performance in MnxFe3–xO4/PANI/AC nanocomposites by manipulating the composition of 3d transition metal elements

Radar-absorbing materials (RAMs) have been extensively employed, particularly in national security stealth technologies. Even though various novel RAMs have been invented, iron oxides still attract tremendous attention due to their distinctive properties that are suitable for radar absorption applications. However, some development should still be carried out to improve the feasibility of iron oxides as RAMs. In this study, iron oxides-based RAMs were developed by manipulating the composition of 3d metal transition elements, Fe and Mn, which mainly influence the radar absorption performance. We also use a compositing strategy by combining this iron oxide with conductive and dielectric polymer materials to form MnxFe3–xO4/PANI (polyaniline)/AC (activated carbon) nanocomposites. The nanocomposites were synthesized using the coprecipitation method with x = 0–1.2 variation. The RAMs functioned in the X-band frequency range (approximately 7–13 GHz) with a thickness of 2 mm to 5 mm. The MnxFe3–xO4/PANI/AC nanocomposites were successfully fabricated by various examinations. The Mn2+ ion was incorporated into Fe3O4, while the nanocomposites exhibited sphere-like morphologies constructed from MnxFe3–xO4, AC, and PANI having aggregates, granules, and chains forms, respectively. Moreover, the nanocomposite sample exhibited superparamagnetic properties, with the saturation magnetization decreasing with the increase in the Mn2+. Interestingly, the MnxFe3–xO4/PANI/AC nanocomposites with x = 1.2 showed a high radar absorption performance with a reflection loss of –42 dB at 9.3 GHz, which mainly attributed to the magnetic loss mechanism and, thus, makes the obtained nanocomposite very potential for radar absorption applications. We believe our findings could pave the way for the realization of iron oxide-based RAMs for radar absorption applications.

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection.

Design of Underwater Cables Applicable to Submarine(KSS-III Batch-II) Navigation Radar

Since the creation of the first submarine telegraph cable connecting England and France in 1850, today, due to the rapid development of the Internet and Communications fields, the demand for submarine communication cables has increased and continues to develop. Submarine cables are designed and manufactured to be suitable for the maritime environment, and many parts are imported from overseas, and several domestic cable manufacturers are producing them for commercial purposes. Field-proven and reliable commercial submarine communication cable technology is also being applied to the military field, and is especially actively utilized in naval surface ships and submarines. This paper is about the design of an underwater cable applicable to submarine(KSS-III Bach-II) navigation radar and also is to secure the performance of an underwater cable with bending durability in extreme marine environments, emergency diving, and repeated mast raising and lowering. Finally, we hop that the domestic development of underwater cables will help improve the performance of navigation radars for submarines.

Radar waveform generative design method

AbstractTo address the issue of radar detection performance degradation caused by interference in complex electromagnetic environments, this paper proposes a generative waveform design method and demonstrates its advantages. This method fully exploits waveform degrees of freedom to achieve a larger design space, generating a multitude of agile waveform parameters within the constraint envelope through algorithmic processes. This approach enables the radar to maintain detection performance in complex electromagnetic environments. Compared with the traditional waveform design method, the simulation experiment verifies the effectiveness of the generative waveform design method.

Isolated point clutter suppression method for airborne STAP radar in wind farm environment

With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SOCFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.

MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset.

Radar shielding jamming method based on random phase modulation of digital coding metasurface

In this paper, a novel radar jamming method based on random phase modulation of digital coding metasurface was proposed. For typical radar system, the modulation model of coding metasurface and the echo model are deduced firstly. Then, the ordered statistics greatest of the constant false alarm rate (OSGO-CFAR) detection model was chosen to analyze the radar detection performance. Through serials of simulation, the influence of modulation modes, modulation switching interval, the number of coding elements and the signal-to-noise ratio (SNR) on radar detection performance is analyzed. Finally, the Monte Carlo simulation is performed. The result shows that as the modulation switching interval of metasurface decreases, the spectrum energy distribution of the target becomes more dispersed and the detection probability of the target decreases obviously. Traditional radar detectors will become ineffective, and the shielding of the target will be achieved. This implies a new radar jamming method which can realize target shielding instead of traditional stealth by reducing the radar cross section.

Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework

To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.

Aerodynamic optimization of double S-duct caret intake by self-adapting non-dominated sorting genetic algorithm

The caret intake could not only provide compressed airflow to the aircraft engine stably but also should have applicable radar stealth performance. Radar stealth means that the echo intensity of the intake should be low when detected by radar, which could be measured by radar cross section (RCS). The airflow distribution of the double S-duct caret intake was numerically optimized to improve aerodynamic performance, with radar stealth performance as constraints. Self-adaptive genetic algorithm and Kriging model were used to improve the optimization efficiency. There could be multiple boundary-layer separations in the S-duct caret intake, resulting in obvious total pressure distortion at the outlet and secondary flow. To suppress these adverse airflow phenomena, the optimization of the double S-duct intake was defined as a problem with two targets and multiple constraints. By defining the probabilities of selection and variation as functions of the iteration number and the fitness, the self-adapting non-dominated sorting genetic algorithm can be applicable for such a problem. Using the Kriging model, which was proven to have satisfactory accuracy in predicting performance parameters, an optimized intake with both excellent aerodynamic and stealth performance was obtained, with a 30% reduction in DC60 value and a 47% reduction in SC60 value compared to the intake before optimization.

Unlocking versatile capabilities: Mixed-valence decavanadate aerogels for boosting radar, infrared, and thermal stealth

Multifunctional compatible stealth materials have emerged as the focal point of contemporary protection technology research and vanadium-based nanomaterials play a pivotal role in the development of advanced stealth materials. Here, a compatible stealth aerogel is successfully synthesized by employing mixed-valence decavanadate as the vanadium oxide (VOx) molecular model. Ultralight {VⅣVV9}/MXene aerogel (0.0429 g cm–3) exhibits exceptional radar stealth performance with a minimal reflection loss (RLmin) of −57.74 dB (99.9998% EMW absorption) and a significantly superior radar cross section reduction value of 26.77 dB m2. The aerogel's exceptional properties, including a low infrared (IR) emissivity (0.479) and a low thermal conductivity of (32.30 mW m–1 K–1), are crucial for enabling compatibility with IR and thermal stealth technologies. The presence of a mixed-valence polyoxovanadate cluster leads to an increase in the Schottky barrier and enhances magnetic properties, consequently boosting interfacial polarization and contributing to magnetic losses during electromagnetic wave (EMW) absorption. Consequently, altering the number of valence electrons significantly enhances the compatible stealth capabilities. These findings contribute significantly to our comprehension of how microstructure impacts EMW absorption processes and provide a basis for further research into the development of VOx-based compatible stealth materials.

Enhancing Integrated Sensing and Communication (ISAC) Performance for a Searching–Deciding Alternation Radar-Comm System with Multi-Dimension Point Cloud Data

In developing modern intelligent transportation systems, integrated sensing and communication (ISAC) technology has become an efficient and promising method for vehicle road services. To enhance traffic safety and efficiency through real-time interaction between vehicles and roads, this paper proposes a searching–deciding scheme for an alternation radar-communication (radar-comm) system. Firstly, its communication performance is derived for a given detection probability. Then, we process the echo data from real-world millimeter-wave (mmWave) radar into four-dimensional (4D) point cloud datasets and thus separate different hybrid modes of single-vehicle and vehicle fleets into three types of scenes. Based on these datasets, an efficient labeling method is proposed to assist accurate vehicle target detection. Finally, a novel vehicle detection scheme is proposed to classify various scenes and accurately detect vehicle targets based on deep learning methods. Extensive experiments on collected real-world datasets demonstrate that compared to benchmarks, the proposed scheme obtains substantial radar performance and achieves competitive communication performance.

Fabrication of Al/Ni core–shell pigments via galvanic displacement and their application in radar-infrared compatible stealth coatings

The Al/Ni magnetic core–shell pigments were successfully synthesized in this study using the galvanic displacement method. A dense nickel shell was formed on the surface of a flake Al pigment (Al:Ni2+ = 1:0.5), resulting in a decrease in its lightness by 9 units while only slightly increasing the average infrared emissivity by 0.1 compared with the uncoated flake Al pigments. And it also exhibited soft magnetic characteristics, with a saturated magnetization value of 4.5 emu/g. Moreover, the impact of Al/Ni core-shell pigments on the infrared-radar stealth performance of the target was investigated based on the structure of “low-emissivity coating (LEC) + radar absorbing coating (RAC)”. The results showed that the emissivity of Al/Ni composite coatings maintained a low infrared emissivity (ε ≤ 0.23). The introduction of Ni shell improved the impedance matching between the Al/Ni coating and free space, allowing greater penetration of microwaves into the LEC and improving radar stealth performance of materials. The Al/Ni composite coatings (Al:Ni2+ = 1:0.5, the thickness of RAC:1.2 mm, and LEC: 60 μm) exhibited an extended effective absorption bandwidth (Reflection loss (RL) ≤ −10 dB) from 2.6 to 4.25 GHz and an enhancement of the average RL from −6.43 to −7.1 dB in the range of 2–18 GHz compared to the RAC. Consequently, the Al/Ni magnetic core–shell pigments can be used as novel low-emissivity pigments for infrared-radar compatible stealth research.