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A Multi-Featured Detection Method for small Target on Sea Surface Based on GSP

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Liang, Zhenchuan Chen, Junjie Jiang, Junzheng Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China

The detection of floating small targets is a challenging problem for marine surveillance radar. To effectively detect the floating small target in a complex marine environment, this letter proposes an innovative multi-featured detection method by leveraging the graph signal processing (GSP) theory. With GSP, we propose two kinds of graph representations of standardized Doppler power spectrum (SDPS) to capture the correlation of the radar data in the Doppler domain. Then, by exploiting the graph representations, three quantitative graph features, graph Laplacian regularizer, trace of Laplacian matrix, and variance of self-loop weight, are developed to distinguish target returns from sea clutter. Finally, a detector based on the graph features is constructed by the fast convex hull learning algorithm. Experiments conducted on the measured radar datasets and comparisons with existing methods confirm the effectiveness of the proposed method.

关键词： Detection of floating small targets fast convex hull learning algorithm graph features graph signal processing (GSP)

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A real-time small target detection network

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signal IMAGE AND VIDEO processing 2021年第6期15卷 1265-1273页

作者： Ju, Moran Luo, Jiangning Liu, Guangqi Luo, Haibo Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Opt Elect Informat Proc Shenyang 110016 Liaoning Peoples R China Key Lab Image Understanding & Comp Vis Shenyang 110016 Liaoning Peoples R China McGill Univ Montreal PQ H3A 0G4 Canada

Target detection based on deep convolutional neural network has achieved excellent performance. However, small target detection is still one of the challenges in the field of computer vision. In this paper, we present an efficient network for real-time small target detection. The proposed network performs feature extraction using a modified Darknet53, while utilizing scale matching strategy to select suitable scales and anchor size for small target detection. In the network, we design an adaptive receptive field fusion module to increase the context information around the small targets by merging the features with different receptive field. Furthermore, we also propose an image cropping method in data preprocessing, aiming to make the targets trained in a wider range of scales. We conduct experiments on VEDAI dataset and small target dataset. Comparative results show that the proposed network achieved 74.5% mean average precision (mAP) at 50.0 FPS on VEDAI dataset and 45.7% mAP at 51.1 FPS on small target dataset which is better than other advanced target detectors.

关键词： small target detection Deep convolutional neural network Receptive field data preprocessing

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FANM: Fuzzy-Aware Nested Mamba for Infrared Dense small Target Detection

FANM: Fuzzy-Aware Nested Mamba for Infrared Dense Small Targ...

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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Yi Sui Xiyang Zhi Yuanxin Huang Xiaoyang Liang Zheng Lu Wei Zhang Research Center for Space Optical Engineering Harbin Institute of Technology Harbin China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The task of infrared dense small target detection aims to accurately locate densely distributed thermal radiation targets in complex scenes. However, in complex scenes, dense small targets often suffer from occlusion, shadows, and blurriness, leading to unclear detection bounding boxes and inaccurate localization. To address the aforementioned issues, this paper introduces an innovative approach for infrared dense small target detection, termed the Fuzzy-Aware Nested Mamba (FANM) network. Specifically, the network takes a single-frame infrared image as its input and leverages shallow, middle, and deep Mamba networks to fully exploit and integrate local and global multi-scale features. The extracted features are subsequently passed to a feature pyramid network (FPN) to balance the spatial resolution and semantic information of each feature map layer, ensuring that feature maps of different scales can be effectively utilized. Then, the output features at each level are fed into the detection head, which consists of two branches: one for regressing the positions of arbitrarily distributed bounding boxes, and the other for predicting the quality of the bounding boxes. The proposed fuzzy perceptual loss function is used to optimize these branches, ultimately outputting images with accurately detected target bounding boxes. Experiments were performed on the DMIST dataset, which includes images with varying numbers of targets, and the results validate the effectiveness of our method.

关键词： Location awareness Head Accuracy Uncertainty Semantics Object detection Feature extraction data mining Spatial resolution Optimization

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HRTD: A high-precision rotating target detector for SAR scenes

HRTD: A high-precision rotating target detector for SAR scen...

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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Zheng Zhou Yijun Li Zongyong Cui Yiming Pi Zongjie Cao University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the past, synthetic aperture radar (SAR) target detection research has mainly focused on the representation of bounding boxes. However, the distant contextual prior knowledge in SAR images has not been fully utilized, leading to challenges in detecting small targets and densely arranged targets with multi-scale characteristics. Therefore, this paper proposes a high-precision rotating target detector (HRTD) for SAR scenarios to address the aforementioned issues. More precisely, we introduce a multi-scale rotation-equivariant network (MRNet) and align rotation-invariant regions of interest to enhance target orientation prediction capability. The multi-scale dynamic global attention (MDGA) in MRNet can dynamically adjust the contextual multi-scale spatial receptive field in real-time, facilitating the comprehensive extraction of distant contextual features. This method achieves significantly better detection performance on the SSDD+ and SRSDD-v1.0 datasets compared to other algorithms.

关键词： Heuristic algorithms Detectors Object detection Feature extraction Prediction algorithms data processing Real-time systems Radar polarimetry data mining Synthetic aperture radar

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海空背景下低慢小目标泛探雷达多域多维特征建模与分析

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信号处理 2024年第5期40卷 801-814页

作者：邓振华陈小龙薛伟张月汪兴海关键赵志坚哈尔滨工程大学烟台研究院山东烟台264000 海军航空大学山东烟台264001 中山大学电子与通信工程学院广东深圳518107

飞鸟和无人机等“低慢小”目标回波微弱、特征不明显,对雷达探测和识别提出了很高的要求,对其特征建模与特性分析是基础,而获取雷达多域多维的目标特征是前提。数字阵泛探雷达通过“宽发窄收”工作模式,实现目标的长时间积累,实现更高... 详细信息

飞鸟和无人机等“低慢小”目标回波微弱、特征不明显,对雷达探测和识别提出了很高的要求,对其特征建模与特性分析是基础,而获取雷达多域多维的目标特征是前提。数字阵泛探雷达通过“宽发窄收”工作模式,实现目标的长时间积累,实现更高的积累增益和多普勒分辨率,能够获得目标的多域多维特征,为“低慢小”目标的精细化处理和探测识别一体化奠定了基础。该文针对海空背景下的飞鸟、旋翼和固定翼无人机、直升机等“低慢小”目标,利用数字阵泛探雷达系统获得的距离-方位-帧间、距离-脉冲-帧间等多维数据,提取目标的时域回波特征(单帧脉冲回波、动态脉冲回波)、变换域多普勒特征(多普勒瀑布图、微多普勒谱)、长时间机动特征(加速度序列、加加速度序列、航迹)7类19种多域多维特征,能够充分的反映目标在单帧和多帧数据间的幅值起伏、能量变化、运动、机动、微动等特性,从而实现对“低慢小”目标的精细化特性描述与分析。最后,采集并构建了数字阵泛探雷达“低慢小”目标特征数据集,对典型目标的特征进行验证和定量、定性分析,总结不同目标的特征差异,验证结果表明,四种类型的“低慢小”目标的多维特征具有明显的区别,获得的特征和差异将为后续的“低慢小”目标分类和识别提供重要支撑。

关键词： “低慢小”目标数字阵泛探雷达回波特征多普勒特征机动特征

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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries

Open Set HRRP Recognition Using Adaptive Learning with Enclo...

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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Binhua Yu Ping-An Liu Yichen Liu Hongyu Wang Liang Zhang Yanhua Wang Kun Liu Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target’s pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method.

关键词： Training Sensitivity Image resolution Image recognition Target recognition databases Imaging Radar applications signal resolution Dispersion

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Target Location in Distributed MIMO radar based on Coherent processing

Target Location in Distributed MIMO radar based on Coherent ...

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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Fa Wei Shi Yan Shengqi Zhu Lan Lan Ximin Li Jiawei Qi Runlong Ma National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Remote Sensing Device Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In this paper, the problem of target location is investigated in the distributed multiple-input multiple-output (MIMO) radar based on coherent processing. At the modelling stage, the distributed MIMO radar is divided into three separated subarrays, where both transmit and receive functions are used in subarrays 1 and 3, while only receive function is adopted in subarray 2. Furthermore, a small frequency increment is introduced among different antenna elements in the subarray 1, in the meantime, a phase modulating operation is performed for various antenna elements in the subarray 3. In this respect, the target location performance can be improved significantly by means of the transmit modulation in subarrays 1 and 3. Under the circumstances, the model of target location is established, and a two-step estimation technology is shown, where a small location sector with respect to targets are firstly obtained, and the accurate location are further estimated. Specifically, in order to reduce the computational cost required by the multi-dimensional search procedures, an alternating procedure is developed by a sequence of 1-D searches obtained alternating between the optimization over each variable keeping the other fixed. Numerical results corroborate the effectiveness of the developed methods for the target location with distributed MIMO radar based on coherent processing.

关键词： Frequency modulation Phase modulation MIMO radar Estimation Search problems data processing Encoding Optimization Antennas MIMO communication

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A Virtual Aperture Extension Method for Shipborne HFSWR Based on RD-Domain Spatiotemporal data Block Extrapolation

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2025年 3卷 818-831页

作者： Youmin Qu Xingpeng Mao Zhibo Tang Yiming Wang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Key Laboratory of Marine Environmental Monitoring and Information Processing Ministry of Industry and Information Technology Harbin China Laboratory of Marine Physics and Remote Sensing First Institute of Oceanography Ministry of Natural Resources Qingdao China

To enhance the maneuverability and extend the detection range of high-frequency surface wave radar (HFSWR), shipborne systems have been developed as an alternative to shore-based platforms. However, the limited space on shipborne platforms results in a small radar array aperture, which consequently diminishes the radar’s direction-of-arrival (DOA) estimation performance. Additionally, the target echoes received by HFSWR are often accompanied by a large amount of strong clutter. Traditional extrapolation-based aperture extension methods fail because they cannot effectively distinguish between targets and clutter. Therefore, how to extend the aperture of shipborne HFSWR remains a problem to be addressed. To overcome these challenges, we improved conventional extrapolation-based aperture extension techniques by incorporating the signal processing workflow of HFSWR and proposed a novel aperture extension method for uniform linear arrays, based on range-Doppler domain spatiotemporal data block extrapolation (RDSDBE). Specifically, on the one hand, we extend the array aperture in the range-Doppler (RD) domain to address the failure of traditional aperture extension methods in the presence of strong clutter. On the other hand, we segment the target echoes in the time domain to tackle the issue of large aperture extension errors caused by the limited number of snapshots in shipborne scenarios. Through simulation and experimental data, we validated the proposed RDSDBE method and analyzed its performance.

关键词： Apertures Radar Direction-of-arrival estimation Arrays Estimation Extrapolation Clutter Antenna arrays Time-domain analysis Array signal processing

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A Low-Altitude small Target Radar Recognition Algorithm Based on Micro-Doppler Features and Vision Transformer Network

A Low-Altitude Small Target Radar Recognition Algorithm Base...

引用

signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Guangmao Chen Haibo Liu Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan 250300 China Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In low-altitude airspace surveillance scenarios, accurately distinguishing between birds and unmanned aerial vehicles (UAVs) is of vital importance. However, the high similarity in flight altitude and speed between birds and UAVs makes it extremely difficult to distinguish between these two types of targets. To enhance radar recognition performance for both UAVs and birds, this paper proposes a low-altitude target recognition algorithm that combines micro-Doppler features with a Vision Transformer network (ViT) under a staring radar system. Verified with real-measured data, the recognition accuracy for three types of UAVs and birds reaches 94%.

关键词： Computer vision Accuracy Target recognition Surveillance Radar Birds Transformers data processing Robustness Classification algorithms

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Multi-view Bistatic SAR Automatic Recognition Study based on Ground small Target SAR dataset

Multi-view Bistatic SAR Automatic Recognition Study based on...

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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Jiming Lv Daiyin Zhu College of Electronics and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Faculty of Engineering Niigata University Niigata Japan Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

MiniSAR systems possess unique advantages in ground target detection compared to traditional spaceborne and airborne SAR systems, mainly in terms of lower cost, real-time performance, and portability. Although numerous studies have focused on target recognition based on SAR systems with unmanned aerial vehicle, most are limited to monostatic SAR systems. Bistatic SAR systems offer stronger stealth capabilities compared to monostatic systems, as they can receive electromagnetic reflections from any angle. However, due to the lower intensity of received electromagnetic signals in bistatic systems, targets in SAR images tend to experience deformation and attenuation. In this study, based on independently developed monostatic and bistatic SAR systems, we study the integration of multi-view and large-scale deep learning models to aiming to address the issue of hard to recognized small targets. Additionally, the paper validates our proposed method on the publicly available MSTAR dataset and the self-developed FAST-Vehicle dataset. Experimental results demonstrate that the proposed multi-view target recognition method effectively identifies SAR bistatic deformed targets.

关键词： Training Image recognition Accuracy Target recognition Imaging Object detection Transformers Radar polarimetry Real-time systems Electromagnetics

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