Radar Detection Research Articles

Self-supervised network for oriented synthetic aperture radar ship detection based on self-distillation

Self-supervised network for oriented synthetic aperture radar ship detection based on self-distillation

Main lobe deceptive jamming suppression based on blind source separation and energy detection for monopulse radar

AbstractMain lobe deceptive jamming always causes the serious degradation of signal detection ability and angle measurement precision of monopulse radar. In recent years, the Blind Source Separation (BSS) method has been adopted to suppress the main lobe jamming. However, the separation results of BSS have the problem of amplitude ambiguity, which will cause the radar using monopulse angle measurement fail to measure the angle parameters after jamming suppression. To tackle this problem, a novel main lobe jamming suppression method is proposed based on BSS and energy detection. Firstly, the models of target echoes and jamming in the sum and difference beam receiving channels are derived. Secondly, the target echoes and interferences are separated by the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm, and then the unperturbed signal segments in the mixed signal are extracted by energy detection, thereby obtaining the precise ratio of the sum and difference channels to complete the angle measurement. Performance of the method was verified by numerical simulation. The results show that the proposed method can achieve interference suppression while accurately estimating the angle parameter of the target.

Temporal-enhanced Radar and Camera Fusion for Object Detection

Recently, object detection methods based on multimodal fusion have gained widespread adoption in autonomous driving, proving to be valuable for detecting objects in dynamic environments. Among them, millimetre wave (mmWave) radar is commonly utilized as an effective complement to cameras, as it is almost unaffected by harsh weather conditions. However, current approaches that fuse mmWave radar and camera often overlook the correlation between the two modalities, failing to fully exploit their complementary features. To address this, we propose a temporal-enhanced radar and camera fusion network to explore the correlation between these two modalities and learn a comprehensive representation for object detection. In our model, a temporal fusion model is introduced to fuse mmWave radar features from different moments, thus mitigating the problem of mmWave radar point-object mismatch due to object movement. Moreover, a new correlation-based fusion strategy using the dedicated mask cross attention is proposed to fuse mmWave radar and vision features more effectively. Finally, we design a gate feature pyramid network that selects shallow texture information based on deep semantic information to obtain more representative features. The experimental results on the nuScenes benchmark demonstrate the effectiveness of our proposed method.

Enhancing RODNet detection in complex road environments based on ESM and ISM methods

In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.

Wideband FMCW radar for long-distance target detection based on photonic chirp pulse stitching.

Current wideband frequency-modulated continuous-wave (FMCW) radar fails in detecting long-distance target, since only the target whose echo is temporally close to the reference linear frequency modulation (LFM) chirp can be detected. We propose a long-distance target detection method for the wideband FMCW radar based on photonic chirp pulse stitching. The stitching photonic chirp pulse that is generated through optical up- and downconversion of the LFM is employed as local signal for the de-chirping process instead of the LFM itself, as currently used. By adjusting the transmitting signal period, a theoretically arbitrarily large detection distance can be obtained with the proposed method. Experiments obtain high-resolution range profiles for two targets located at 1.44 km and 14.5 km, respectively, proving the feasibility of the proposed scheme.

Fine spatio-temporal prediction of fishing time using big data

Overfishing, bycatch, and other anthropogenic threats may lead to the destruction of fragile habitats and substantial losses of marine life. Marine fishery resources can be protected by adjusting fishing intensity and establishing marine reserves. Currently, China adopts the closed fishing season management approach to protect traditional fishing grounds, where fine spatio-temporal prediction is essential to efficiently supervise the wide scope. Fishing vessel behaviors reflect fishers’ experience as well as the information provided by fish detection radar, while the fishery resource distribution is relevant to the marine environment. In this study, we identified fishing vessel behaviors (gillnets, trawls, purse seines, and abnormal behaviors) and qualitatively assessed and predicted fishing time of different fishing vessel behaviors to search for high intensity fishing operation areas by constructing a time-space prediction model. The model was based on big data of fishing vessel automatic identification systems and3 the marine environment, and was verified in the East China Sea. The prediction results generally corresponded with the distribution of traditional fishery resources in the East China Sea and the fishing efforts provided by the Global Fishing Watch. This model can provide an accurate and effective refined fishing vessel operation time prediction, and benefits fishing management and fishery resources protection.

Effectiveness evaluation of cooling measures for express highway construction in permafrost regions based on GPR and ERT

Global warming and human activities are accelerating the degradation of permafrost on the Qinghai-Tibet Plateau (QTP), leading to significant settlement and cracking issues in the local express highway infrastructures. In response, the Gonghe-Yushu Express Highway (GYE) on the east edge of the QTP incorporated extensive cooling measures during its construction to enhance embankment stability. Despite these efforts, field investigations have disclosed that embankment diseases persist across various sections, including those with implemented cooling measures. This study focuses on a specific test and demonstration section of the GYE, employing a suite of cooling measures to assess their engineering effectiveness. Utilizing a combination of multi-time ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) detection, alongside on-site disease investigations and temperature monitoring, this research comprehensively evaluates the efficacy of different cooling interventions. Findings indicate that although cooling measures generally curb permafrost degradation in areas with ice-rich and ice-saturated soils, they fall short in sections with massive ground ice. Of the six cooling measures examined in the demonstration section, ventilation duct embankments emerge as the most effective, whereas crushed-rock layer embankments rank as the least. The study further reveals that the combined use of XPS insulation boards and two-phase closed thermosyphons inadequately addresses the issue of central heat accumulation in broad-width express highways, reducing uneven settlement issues but aggravating longitudinal cracking. Comparative analysis of on-site surveys and monitoring data suggests that regular application of GPR and ERT techniques can proficiently assess the performance of cooling measures.

Multifunctional Metamaterial with Reconfigurable Electromagnetic Scattering Properties for Advanced Stealth and Adaptive Applications.

The rapid development of radar detection systems has led to an increased sensitivity to the electromagnetic (EM) scattering properties of detected targets. Flexible and adaptable EM scattering properties significantly enhance the survivability of battlefield weapons. This paper presents the design of a novel multifunctional metamaterial with reconfigurable EM scattering properties based on a bistable curved beam. In addition to the cushioning and energy absorption properties of curved beams, the metamaterial achieves more than 90% EM absorption in the frequency range of 2.17-17.31GHz, with a relative thickness of only 0.09λL. The bistable nature of the metamaterial allows it to switch between different states. Moreover, combined with the digital coding, this metamaterial can continuously adjust the absorbing bandwidth and further enhance the EM absorption rate within a specific frequency band range. If applied to satellite configurations, the developed metamaterial significantly reduces the radar cross section and offers potential applications in reconfiguring EM scattering properties, when applied to satellite configurations. By actively controller and reconstructing the EM scattering properties at certain frequency points, the metamaterial can achieve camouflage, providing innovative solutions for future stealth technology, electronic countermeasures, and deception jamming in radar detection.

Multiresolution Hierarchical Target Detection in High-Resolution Maritime Surveillance Radars

Multiresolution Hierarchical Target Detection in High-Resolution Maritime Surveillance Radars

Wi-Fi-Based Posture Imaging Radar for Vital Sign Monitoring and Fall Detection

Wi-Fi-Based Posture Imaging Radar for Vital Sign Monitoring and Fall Detection

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.

Advanced Copula-based Methods for Nonparametric Detection and Characterization of Wideband Radar Signals

This paper introduces advanced copula-based methods for the nonparametric detection and characterization of wideband radar signals. The research focuses on developing signal detection algorithms that are invariant to changes in the probability density function of the sounding or reflected signals, employing multiscale analysis techniques and copula-based statistics. Two primary approaches are explored: multiscale analysis using wavelet transforms and rank-based signal detection with copula-based ambiguity functions. Simulation results confirm the effectiveness of the proposed approaches. The research demonstrates that integrating rank-based methods with copula-based statistics significantly improves the detection and analysis of wideband radar signals, particularly in complex scenarios where signals exhibit intricate dependency structures. This comprehensive detection framework is well-suited for handling high-dimensional radar signal data, enhancing accuracy and reliability under varied conditions. Future work will focus on optimizing copula selection and permutation strategies to further improve performance.

Ground penetrating radar clutter suppression based on the weighted tensor Schatten p-paradigm minimization of tensor principal components

The issue of clutter suppression in ground-penetrating radar (GPR) detection has consistently been a prominent area of research. The existing tensor robust principal component analysis (TRPCA) approach encounters a challenge when handling different singular values, whereby all singular values are reduced to the same degree, leading to loss of information in the low-rank part. To address the aforementioned issues, this study developed a TRPCA method based on weighted tensor Schatten p-paradigm minimisation (p-TRPCA). This approach assigns distinct weights to each singular value by using a weighted tensor Schatten p-paradigm, facilitating the optimal decomposition of GPR data into low-rank clutter and sparse target components. The processing of target echoes is then completed by applying a threshold judgement to the results. The efficacy of the proposed method was validated through simulations and real-world testing. Its performance was benchmarked against established advanced clutter removal techniques in terms of peak signal-to-noise ratio (PSNR) and signal-to-clutter ratio. The findings demonstrated that the method effectively delineated the low-rank clutter matrix and sparse target matrix within the B-scan image and exhibited superior SNR compared to other methods.

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.

Field validation of effects of species and flock size on echoes in avian radar surveys

Radar is a powerful technology for surveys of avian movements. Validating the accuracy of radar detection is essential when establishing quantitative criteria for tracking bird trajectories and counting bird flocks. This study clarifies the positional and biological factors influencing the probability of detection (POD) and echo size on X-band marine radar. The bird trajectory for validation was obtained by ornithodolite at the same time as the radar scan. Distance was found to have a negative effect on POD and echo size, while elevation angle positively affected POD. Body mass and flock size positively affected POD and echo size. In predicting detection performance, the survey distance required to achieve 50% POD was 750 m or less for Grey-faced Buzzard, the lightest target species, but up to 1800 m for a pair of Bewick’s Swan. Our study provides survey and analysis procedures that allow for efficient validation using ornithodolites. Then, we identify the range settings that should be considered for target species and contribute to establishing criteria for quantitative radar bird surveys.

Adaptive radar target detection in nonzero-mean compound Gaussian sea clutter with random texture

This paper deals with the radar target detecting problem in nonzero-mean compound Gaussian sea clutter with random texture. The texture is considered to be an inverse Gamma, Gamma, or inverse Gaussian variable. Three novel adaptive detectors using the two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) are proposed. More precisely, we derive the test statistics of the proposed detectors for known mean vector (MV) and speckle covariance matrix (CM) in the first step. In the second step, unbiased and consistent estimators are proposed to estimate the MV and CM in nonzero-mean compound Gaussian circumstances. We acquire the fully adaptive nonzero-mean GLRT detectors by substituting the estimates into the test statistics. Then, the constant false alarm rate (CFAR) properties of the proposed detectors with respect to (w.r.t.) the speckle CM are proved. Finally, the performance of three proposed detectors is verified by simulation experiments using the synthetic and real sea clutter data.

UAV Swarm Target Identification and Quantification Based on Radar Signal Independency Characterization

Radar surveillance of noncooperative UAV swarm is challenging and is involved in many critical surveillance scenarios. The multimodality property of dynamic UAV swarm targets presents larger radar signature complexity and elevates the radar detection difficulty. The swarm unit number ambiguity from dense UAV grouping also inhibits radar monitoring accuracy. Inspired by the coherent integration essence of swarm target signals, this paper proposes a radar signal processing framework based on complex valued independent component analysis (cICA) for swarm target identification and quantification. The target detection threshold is determined from pure clutter signals after cICA processing. A customized clustering algorithm is applied on independent components for swarm target quantification. Target detection and quantification methods are verified with various multimodality UAV swarm flight plans. The results indicate that the detection performance of the proposed method is comparable with conventional CFAR algorithms with better stability performance. The target quantification procedure could estimate swarm unit numbers with acceptable numerical deviations. More discussions are given on the relevance between quantification accuracy and swarm configurations with respect to signal independency mechanisms. Efficiency discussions reveal the bottleneck of the proposed method for future optimization works.

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.

Ship target detection in SAR images based on SimAM attention YOLOv8

AbstractDeep learning has been widely applied in ship detection in synthetic aperture radar (SAR) imagery due to their powerful feature representation capabilities. However, YOLOv8 models treat all regions of the image equally during convolutional feature processing, resulting in less‐than‐ideal outcomes. To address this limitation, this study proposes a simple, parameter‐free attention module (SimAM) attention‐based YOLOv8 algorithm for ship detection in SAR images. The proposed algorithm first passes through a backbone network, which incorporates SimAM attention modules. The SimAM attention mechanism successfully allocates the convolutional neural network's 3D weights effectively using an energy function method, without introducing additional parameters. This mechanism enables the network to automatically emphasize key features in the image, enhancing its ability to represent target areas and suppress background interference. Subsequently, deep features are upsampled and fused with relatively shallow features to extract features at three different scales and achieve target detection, ultimately outputting classification and positional information of the targets. The effectiveness of the model on the SAR‐ship‐dataset is experimentally validated achieving an mAP50 value of 97.72% and an mAP50‐95 value of 68.99%, confirming the superiority of the proposed model.

Emergent Technologies in Human Detection for Disaster Response: A Critical Review

In the face of natural disasters, the rapid detection of trapped victims is crucial for effective search and rescue (SAR) operations. This review critically examines the emergent technologies in human detection that are pivotal in disaster response. Current methodologies for human detection encompass a variety of techniques: Radar, Artificial intelligence (AI), Drones, Robotics, Gas sensors, Acoustic, Infrared, and Optical detection. These methods are specifically designed to detect human presence in disaster scenarios by leveraging principles from electromagnetic, acoustic, AI-based algorithms, gas sensors, and optical detection. However, these technologies face challenges and limitations. They often depend on environmental conditions, encounter obstacles that affect their penetration capabilities, and have limited spatial resolution due to complex backgrounds. This paper aims to provide a comprehensive overview of the state-of-the-art in human-detection technologies, assess their impact on SAR missions, and offer insights into future advancements that could further enhance the survivability of disaster-stricken individuals.