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检索条件"任意字段=Signal and Data Processing of Small Targets 1995 Conference"

共 783 条记录，以下是71-80 订阅

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data-dependent Channel Selection Method for STAP based on MVDR Criterion

Data-dependent Channel Selection Method for STAP based on MV...

引用

IET International Radar conference 2023, IRC 2023

作者： Li, Yu Chen, Jinming Cheng, ZiYang Wang, Weiwei Zhang, Jun Wu, Tao Duan, Chongdi Xi'an Institute of Space Radio Technology No.504 East Changan Street Changan district Xi'an China University of Electronic Science and Technology of China No. 2006 Xiyuan Street West Gaoxin district Chengdu China

ISBN: (纸本)9781839539954

Space-time adaptive processing (STAP) combines the returned signals from all elements of an antenna array and all coherent pulse trains during a CPI period to provide clutter suppression and target detection ability, which is a promising technique for acquiring small, slow, and moving targets. In this work, we proceed to the data-dependent channel selection method for Post-Doppler beam- space STAP to improve clutter suppression performance in sample starved environments, in which no additional a prior information is used in our framework. The main innovation of this manuscript lies in the characteristic that the selected channels in the fully-dimension weight vector, which is inversely transformed from the RD weight vector, is sparse relative to the whole beam-Doppler channels. Compared with several existing methods, the proposed method significantly enhances the STAP performance in sample starved environments. © The Institution of Engineering & Technology 2023.

关键词： Space time adaptive processing

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

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

引用

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

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

引用

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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Target Location in Distributed MIMO radar based on Coherent processing

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

引用

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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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries

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

引用

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

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

引用

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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AugTarget data Augmentation for Infrared small Target Detection

AugTarget Data Augmentation for Infrared Small Target Detect...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shengjia Chen Jiewen Zhu Luping Ji Hongjun Pan Yuhao Xu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Sichuan University Chengdu China

Sample shortage has always been a frequently-faced problem for the machine-learning models in infrared small target detection. As one of main limitations, it is hampering the further promotion of target detection performance. In this paper, we propose a simple and effective data augmentation scheme, AugTarget, to address this shortage issue of small target samples. Our scheme mainly consists of two crucial algorithms: target augmentation and batch augmentation. The former is designed to generate sufficient targets, by random target representation. The latter is devised to diversify training samples. Moreover, the initially-generated image samples of small targets are further enriched by randomly aggregating the feature representation of different images. The experiments on public datasets demonstrate that our AugTarget could bring an obvious improvement to mean intersection over union (mIoU). Cooperated by the augmentation of our AugTarget, the state-of-the-art (SOTA) method, AGPC could even achieve a distinct performance promotion by 3.03%, 2.17% and 2.63% on MDFA, SIRST-Aug and Merged datasets, respectively. In addition, the experimental results on three baseline models also show the universality & adaptivity of AugTarget to different dataset augmentation. Our source codes are available at https://***/UESTC-nnLab/AugTarget.

关键词： Training Adaptation models Source coding signal processing algorithms Object detection Machine learning signal processing

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Dual-Level Contrast Enhancement Method for Infrared small Target Detection

Dual-Level Contrast Enhancement Method for Infrared Small Ta...

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

作者： Haotian Sun Wenzheng Wang Yuhan Xu Qiuyu Jin Zipeng Zhang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the field of target detection, infrared small target detection(ISTD) is a critical area of research, playing a vital role in reconnaissance, guidance, and early warning systems. Existing spatial Filtering algorithms perform well in clean backgrounds; however, as these filters require manual design based on prior knowledge, they are prone to interference from complex scenes (e.g., clouds, buildings), leading to target omission. small targets typically exhibit blurry features and weak energy, making direct detection susceptible to interference from high-intensity areas, which may result in missed detections. To tackle these challenges, this paper presents a two-tier contrast improvement method (DLCE). First, a gradient filter group is used to enhance the signal-to-noise ratio (SNR) of the target, and then, built upon the concept of local contrast measurement (LCM), a novel intensity calculation method is developed. The new contrast window design effectively enhances the target’s SNR. Compared with existing algorithms, like MPCM (Multi-Scale Patch-based Contrast Measurement) on publicly available datasets, the proposed method more effectively enhances the target’s radiative intensity while suppressing background noise. The effectiveness of the proposed algorithm is confirmed by the simulation results.

关键词： Filters Simulation Object detection Interference Reconnaissance Robustness Windows Noise measurement signal to noise ratio Visual perception

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Fast small Object Detection Algorithm Based on Feature Enhancement and Reconstruction 13

Fast Small Object Detection Algorithm Based on Feature Enhan...

引用

13th International conference on Wireless Communications and signal processing, WCSP 2021

作者： Huo, Zhiyong Yan, Tianwen Cao, Weiye Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

small object detection is one of the research hotspots and challenges in the field of machine vision. Because small targets have fewer pixels in the image, the number of features captured by the first few layers of the target detection network is very small, and the high-level semantic information such as feature classification disappears quickly, making the accuracy of target detection relatively low. The paper uses sub-pixel convolution to reconstruct feature information, uses SE (Squeeze-and-Excitation) attention mechanism to enhance features, and uses U-Net network to capture feature context information and has the advantages of faster detection speed. The algorithm realizes the fast and accurate identification of small targets. Numerical experimental results on the COCO data set and the SUN data set show that the algorithm improved performance by more than 12% in the case of the bounding box AP compared with mainstream algorithms such as YOLOv4. © 2021 IEEE.

关键词： Convolution

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