Radar Performance Research Articles

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.

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

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

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.

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.

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.

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.

Radar detectability of light aircraft micro‐Doppler modulation

AbstractThe critical role of specifying micro‐Doppler mode performance in the modelling and development of modern radar systems is investigated. The authors focus on the detection of micro‐Doppler modulation from light aircraft, analysing data from eight helicopters and nine propeller aircraft. With the growing need for accurate target classification in radar technology, incorporating micro‐Doppler detection metrics into radar performance specifications has become increasingly important. This research offers a novel approach to measuring the detectability of micro‐Doppler modulation relative to returns from the main fuselage. The investigation covers the impacts of various preprocessing techniques, polarisation, and aspect angle on detection capabilities. Findings reveal that, on average, micro‐Doppler modulation from propellers is detectable at distances between 50% and 100% of the range at which the fuselage is detected. For helicopters, this range decreases to between 30% and 80%. Additionally, the study introduces empirically derived statistical models designed to predict micro‐Doppler detection ranges in relation to fuselage returns, enhancing the predictability and specificity of radar system performance. This novel contribution presents a basis for improving radar system specifications, leading ultimately to more predictable and reliable light aircraft classification.

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function.

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance.

Dimensional tolerance optimization of SAR antennas with uncertainty quantification and reliability analysis based on structural-electromagnetic coupling model

The dimensional tolerances greatly impact the structural-electromagnetic performance of spaceborne synthetic aperture radar (SAR) antenna. However, few works have conducted the tolerance allocation of the extendible support structure to guarantee system reliability. To bridge the above gap, an integrated optimization methodology is proposed to design the dimensional tolerances with the structural-electromagnetic coupling model in this study. First, the absolute nodal coordinate formulation is developed to predict the structural deformation triggered by the dimensional deviations. Then, the coupling relationship between the displacement field and the electromagnetic field is elucidated for the SAR system. Subsequently, taking advantage of the surrogate model and the parallel update criterion, the interval uncertainty quantification stemming from dimensional tolerances is executed to quickly identify the worst-case scenarios of structural response. Moreover, a multi-point selection strategy is incorporated into the adaptive Bayesian probabilistic integration with active learning, which significantly improves the computational efficiency of electromagnetic reliability. Based on the above tolerance analysis, the inverse optimization design of the dimensional tolerance is realized by the particle swarm algorithm. Finally, the effectiveness and superiority of the proposed methods are validated in the case study. Our innovative framework and analytical methodology not only furnish a comprehensive structural-electromagnetic coupling model but also transition the design paradigm from experiential to robust tolerance allocation.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Power Allocation Scheme for Multi-Static Radar to Stably Track Self-Defense Jammers

Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance.

Surrogate-based integrated design optimization for aerodynamic/stealth performance enhancements

The shape optimization considering both aerodynamic and radar stealth performances has been a considerable attention area for next-generation aircraft, with challenges involving balancing design contradictions and improving optimization efficiency. This paper develops a surrogate-based integrated design optimization method coupling Computational Fluid Dynamics (CFD) and Computational Electromagnetics (CEM) for aerodynamic/stealth performance enhancements. The topology-based 3D hexahedral mesh generation and 2D triangular mesh generation with adaptive refinement provide efficient preprocessing for CFD/CEM solvers. The surrogate-based optimization algorithm with parallel infill-sampling strategy and adaptive design space expansion is employed to search the global optimum and improve optimization efficiency while filtering out numerical noise. The Weighted Sum method is employed to address design contradictions in different disciplines. Optimizations considering aerodynamic, stealth, and aerodynamic/stealth performance of a flying wing aircraft are conducted to verify the effectiveness of the developed method. The optimization results indicate that exclusive consideration of a singular discipline leads to a significant deterioration in the performance of the other discipline. Balanced performance is achieved through coupling optimization. The numerical noise in the stealth discipline is markedly higher than in the aerodynamics discipline. The results demonstrate that the developed method is capable of addressing aerodynamic/stealth design optimization problems and significantly reducing the number of CFD/CEM evaluations while maintaining global optimization effectiveness.

Complementarity of Sentinel-1 and Sentinel-2 Data for Soil Salinity Monitoring to Support Sustainable Agriculture Practices in the Central Bolivian Altiplano

Soil salinization will affect 50% of global cropland areas by 2050 and represents a major threat to agricultural production and food sovereignty. As soil salinity monitoring is costly and time consuming, many regions of the world undertake very limited soil salinity observation (in space and time), preventing the accurate assessment of soil salinity hazards. In this context, this study assesses the relative performance of Sentinel-1 radar and Sentinel-2 optical images, and the combination of the two, for monitoring changes in soil salinity at high spatial and temporal resolution, which is essential to evaluate the mitigation measures required for the sustainable adaptation of agriculture practices. For this purpose, an improved learning database made of 863 soil electrical conductivity (i.e., soil salinity) observations is considered for the training/validation step of a Random Forest (RF) model. The RF model is successively trained with (1) only Sentinel-1, (2) only Sentinel-2 and (3) both Sentinel-1 and -2 features using the Genetic Algorithm (GA) to reduce multi-collinearity in the independent variables. Using k-fold cross validation (3-fold), overall accuracy (OA) values of 0.83, 0.88 and 0.95 are obtained when considering only Sentinel-2, only Sentinel-1 and both Sentinel-1 and -2 features as independent variables. Therefore, these results highlight the clear complementarity of radar (i.e., Sentinel-1) and optical (i.e., Sentinel-2) images to improve soil salinity mapping, with OA increases of approximately 10% and 7% when compared to Sentinel-2 and Sentinel-1 alone. Finally, pre-sowing soil salinity maps over a five-year period (2019–2023) are presented to highlight the benefit of the proposed procedure to support the sustainable management of agricultural lands in the context of soil salinization on a regional scale.

Minimum peak sidelobe ratio filter for MIMO radar via convex optimization

Multiple input multiple output (MIMO) radar retains transmitting freedom by transmitting multiple waveforms simultaneously. However, due to the cross-correlation between waveforms, the performance of range sidelobes is degraded. So, low sidelobes techniques are especially demanded in MIMO radar. This article proposes a mismatched filtering method for minimizing MIMO radar’s peak sidelobe level ratio (PSLR). In this method, we formulate the problem as a convex optimization (CO) problem and thus ensure its solution is globally optimal. To apply mismatched filters to MIMO radar, we introduce the traditional strategy and propose a new one. Instead of attempting to minimize all auto-correlations and cross-correlations of all waveforms as in the traditional strategy, our proposed strategy directly optimizes the mismatched filter of the synthesis signal for each considered direction. To verify the effectiveness of our method, numerical simulations are carried out. Firstly, the PSLR effects of those two strategies are compared. The results indicate that our proposed strategy dramatically improves the PSLR performance of MIMO radar compared to the traditional one. Then, the influences of several parameters on the PLSR performance are revealed, which indicates that our method is flexible enough to make trade-offs between PSLR and other parameters.