Search Results (379)

Axial estimation is an important task for detecting non-cooperative space targets in orbit, with inverse synthetic aperture radar (ISAR) imaging serving as a fundamental approach to facilitate this process. However, most of the existing axial estimation methods usually rely on manually extracting and matching features of key corner points or linear structures in the images, which may result in a degradation in estimation accuracy. To address these issues, this paper proposes an axial estimation method for spaceborne targets via ISAR image sequences based on a regression network. Firstly, taking the ALOS satellite as an example, its Computer-Aided Design (CAD) model is constructed through a prior analysis of its structural features. Subsequently, target echoes are generated using electromagnetic simulation software, followed by imaging processing, analysis of imaging characteristics, and the determination of axial labels. Finally, in contrast to traditional classification approaches, this study introduces a straightforward yet effective regression network specifically designed for ISAR image sequences. This network transforms the classification loss into a loss function constrained by the minimum mean square error, which can be utilized to adaptively perform the feature extraction and estimation of axial parameters. The effectiveness of the proposed method is validated through both electromagnetic simulations and experimental data. Full article

Adaptive adversarial attacks, where adversaries tailor their strategies with full knowledge of defense mechanisms, pose significant challenges to the robustness of adversarial detectors. In this paper, we introduce RADAR (Robust Adversarial Detection via Adversarial Retraining), an approach designed to fortify adversarial detectors against such adaptive attacks while preserving the classifier’s accuracy. RADAR employs adversarial training by incorporating adversarial examples—crafted to deceive both the classifier and the detector—into the training process. This dual optimization enables the detector to learn and adapt to sophisticated attack scenarios. Comprehensive experiments on CIFAR-10, SVHN, and ImageNet datasets demonstrate that RADAR substantially enhances the detector’s ability to accurately identify adaptive adversarial attacks without degrading classifier performance. Full article

A distributed network of multiple unmanned aerial vehicles (UAVs) equipped with airborne passive bistatic radar (APBR) can form a passive detection network through cooperative networking technology, a novel passive early warning detection system. Its multi-target tracking performance has a significant impact on situational awareness of the detection area. This paper proposes a passive multi-sensors joint optimization control algorithm based on task adaptive switching, with the aim of addressing the impact of limited UAV sensors’ field of view (FOV) on multi-target tracking performance in APBR networks. Firstly, for a single UAV node, the Poisson Labeled Multi-Bernoulli (PLMB) filter is selected as the local filter of each node, with the objective of obtaining the local multi-target density independently. Subsequently, the consensus arithmetic average fusion rule is employed to address the multi-sensors density fusion problem in APBR networks. This enables the acquisition of the global multi-target density and multi-target tracks of the network. The task adaptive switching mechanism of the nodes is constructed further based on the partially observable Markov decision process (POMDP), and the objective functions for the UAV to perform the search task and the tracking task are derived based on differential entropy, respectively. Ultimately, a multi-node joint optimization control algorithm is devised. The simulation experiment demonstrates that the proposed algorithm is capable of effective control of multiple nodes to solve the multi-target search and tracking problem when the node FOV is limited. This further improves the multi-target tracking and fusion capability of the distributed APBR network. Full article

A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization. Full article

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models. Full article

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance. Full article

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security. Full article

Biomass burning on the South African Highveld annually injects substantial amounts of aerosols and trace gases into the atmosphere, impacting the global radiative balance, cloud microphysics, and regional air quality. These aerosols are transported as plumes over long distances, posing challenges to existing in situ and satellite-based monitoring techniques because of their limited spatial and temporal resolution, particularly in environments with low-level sources. This study aims to develop and validate a novel radar-based methodology to detect, track, and characterise aerosol plumes, addressing the limitations of existing in situ and satellite monitoring techniques. Using high-resolution volumetric reflectivity data from an S-band radar in Pretoria, South Africa, a traditional storm tracking algorithm is adapted to improve plume identification. Case studies of plume events in June and August 2013 demonstrate the radar’s effectiveness in distinguishing lower vertical profiles and reduced reflectivity of plumes compared with storm echoes. The adapted algorithm successfully tracked the spatial and temporal evolution of the plumes, revealing their short-lived nature. Results indicate that radar-derived geospatial characteristics have the potential to contribute significantly to understanding the impacts of plumes on local air quality. These findings underscore the critical need for high spatio-temporal resolution data to support effective air quality management and inform policy development in regions affected by biomass burning. Full article

The time and spatial variability of hail events limit the capability of diagnosing the occurrence and stones’ size in thunderstorms using weather radars. The bibliography presents multiple variables and methods with different pros and cons. The studied area, the Lleida Plain, is annually hit by different hailstorms, which have a high impact on the agricultural sector. A rectangular distributed hail pad network in this plain has worked operationally since 2000 to provide information regarding different aspects of hail impact. Since 2002, the Servei Meteorològic de Catalunya (SMC) has operated a single-pol C-band weather radar network that volumetrically covers the region of interest. During these years, the SMC staff has been working on improving the capability of detecting hail, adapting some parameters and searching for thresholds that help to identify the occurrence and size of the stones in thunderstorms. The current research analyzes a twenty-year period (2004–2023) to provide a good picture of the hailstorms occurring in the region of interest. The main research result is that VIL (Vertically Integrated Liquid) density is a better indicator for hailstone size than VIL, which presents more uncertainty in discriminating different hail categories. Full article

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the $P_{fa}$ is set at 0.0001, the proposed method exhibits an average improvement in $P_D$ of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the $P_{fa}$ is set at 0.001, the proposed method exhibits an average improvement in $P_D$ of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR. Full article

The development of autonomous driving vehicles has increased the global demand for robust and efficient automotive radar systems. This study proposes an automotive radar-based ego-vehicle speed detection network (AVSD Net) model using convolutional neural networks for estimating the speed of the ego vehicle. The preprocessing and postprocessing methods used for vehicle speed correction are presented in detail. The AVSD Net model exhibits characteristics that are independent of the angular performance of the radar system and its mounting angle on the vehicle, thereby reducing the loss of the maximum detection range without requiring a downward or wide beam for the elevation angle. The ego-vehicle speed is effectively estimated when the range–velocity spectrum data are input into the model. Moreover, preprocessing and postprocessing facilitate an accurate correction of the ego-vehicle speed while reducing the complexity of the model, enabling its application to embedded systems. The proposed ego-vehicle speed correction method can improve safety in various applications, such as autonomous emergency braking systems, forward collision avoidance assist, adaptive cruise control, rear cross-traffic alert, and blind spot detection systems. Full article

Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship’s contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision. Full article

Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network (MDAF-Net), an advanced framework that significantly enhances image quality and detection accuracy. Firstly, we introduce the Multidirectional Filter (MF), which employs side-window filtering techniques and eight directional filters. This approach supports multidirectional image processing, effectively suppressing speckle noise and precisely preserving edge details. By utilizing deep neural network components, such as average pooling, the MF dynamically adapts to different noise patterns and textures, thereby enhancing image clarity and contrast. Building on this innovation, MDAF-Net integrates multidirectional feature learning with a multiscale self-attention mechanism. This design utilizes local edge information for robust noise suppression and combines global and local contextual data, enhancing the model’s contextual understanding and adaptability across various scenarios. Rigorous testing on six SAR datasets demonstrated that MDAF-Net achieves superior detection accuracy compared with other methods. On average, the Kappa coefficient improved by approximately 1.14%, substantially reducing errors and enhancing change detection precision. Full article

This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers’ positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly. Full article

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection. Full article