检索结果-内蒙古大学图书馆

Conference on Image and signal processing for Remote Sensing XXVII

作者： Sbalchiero, Elisa Thakur, Sanchari Cortellazzi, Marco Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Via Sommar 5 I-38123 Trento Italy

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Radar sounders (RS) play an important role in planetary investigation. However, the complex tasks of predicting performance and interpreting RS data and predicting performance require to perform data simulations. In the literature, there are different methods for RS data simulation, including: i) numerical methods involving the exact solution of Maxwell's equations based on 3D modelling;and ii) methods based on ray-tracing and facet modelling. Among numerical methods, the Finite-Difference Time-Domain (FDTD) technique allows one to precisely model complex nonlinear targets, both geologically and geophysically, at the cost of high computational load. On the contrary, coherent ray-tracing methods reduce the computational cost by triangulating the targets into facets with size comparable to the wavelength. In this work, we combine the advantages of FDTD and ray-tracing methods into a novel integrated simulation technique for modelling scattering phenomena at two scales of wave interaction, i.e. large (facet) scale and small (sub-facet) scale. The coherent facet method is used to simulate facet- scale scattering phenomena, while FDTD is used to evaluate the sub-facet-scale scattering due to roughness on top of the single facets. We investigate the method's effectiveness by generating a one-layer synthetic DEM as a fractional Brownian motion (fBm) process superimposed by different values of RMS slope of the small-scale roughness and by analysing how the received signal changes in terms of signal-to-noise ratio. The results show the effectiveness of the method in representing small-scale roughness realistically.

关键词： Radar sounder simulations FDTD ray-tracing coherent simulator small-scale roughness planetary exploration

来源：评论

学校读者我要写书评

暂无评论

DETECTION OF small MOVING GROUND VEHICLES IN CLUTTERED TERRAIN USING INFRARED VIDEO IMAGERY

DETECTION OF SMALL MOVING GROUND VEHICLES IN CLUTTERED TERRA...

引用

IEEE International Conference on Image processing (ICIP)

作者： Cuellar, Adam Mahalanobis, Abhijit Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665441155

The detection of small moving targets in cluttered infrared imagery remains a difficult and challenging task. Conventional image subtraction techniques with frame-to-frame registration yield very high false alarm rates. Furthermore, state of the art deep convolutional neural networks (DCNNs) such as YOLO and Mask R-CNN also do not work well for this application. We show however, that it is possible to train a CNN to detect moving targets in a stack of stabilized images by maximizing a target to clutter ratio (TCR) metric. This metric has been previously used for detecting relatively large stationary targets in single images, but not for the purposes of finding small moving targets using multiple frames. Referred to as moving target indicator network (MTINet), the proposed network does not rely on image subtraction, but instead uses depth-wise convolution to learn inter-frame temporal dependencies. We compare the performance of the MTINet to state of the art DCNNs and a statistical anomaly detection algorithm, and propose a combined approach that offers the benefits of both data-driven learning and statistical analysis.

关键词： Detection Localization Infrared CNN

来源：评论

学校读者我要写书评

暂无评论

简单野外背景条件下地面雷达对“低慢小”无人机探测数据集

引用

信号处理 2024年第11期40卷 2095-2104页

作者：林钱强秦正阳蒋李兵王旭范红旗国防科技大学电子科学学院自动目标识别重点实验室西安电子工程研究所

无人机因其固有的低雷达散射截面和复杂的飞行模式，给雷达系统对其检测、跟踪与识别提出了严峻挑战，已经成为低空安全领域研究的热点和难点问题。而当前公开发布的雷达目标探测数据集仍十分稀缺，针对“低慢小”目标的雷达回波信号数... 详细信息

无人机因其固有的低雷达散射截面和复杂的飞行模式，给雷达系统对其检测、跟踪与识别提出了严峻挑战，已经成为低空安全领域研究的热点和难点问题。而当前公开发布的雷达目标探测数据集仍十分稀缺，针对“低慢小”目标的雷达回波信号数据集目标类型、运动状态以及试验场景较为单一，数量更显匮乏。针对上述问题，本文通过精心设计试验场景、外场数据录取与数据加工处理，提供了一套简单野外背景条件下地面雷达对“低慢小”无人机探测的数据集。该数据集涵盖了多旋翼和固定翼两类五型无人机目标，航迹包括由远及近、由近及远、进出雷达波束等，目标运动状态包括加速、减速、悬停、爬升、俯冲、航向调整等，数据包含高信噪比、低信噪比等情况，充分考虑了“低慢小”目标特性分析、检测、跟踪、识别等雷达信号处理方法研究对实测数据的迫切需求，为相关学者提供了一套全面且多元化的数据资源，可有效推动雷达低空慢速弱小目标探测与识别技术的发展。

关键词：雷达无人机数据集

来源：评论

学校读者我要写书评

暂无评论

Bioradar signal processing Algorithms based on Machine Learning

Bioradar Signal Processing Algorithms based on Machine Learn...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Huanyi Guo Wuhan University of Bioengineering Wuhan Hubei China

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Since human physiological signals (e.g., respiration and heart rate) generate very small Doppler shifts, they are highly susceptible to being masked by strong clutter backgrounds. The main research focus of this project is to accurately extract weak targets (i.e., vital signs) from complex environments. The signals received by BioRadar contain vital signs signals and clutter signals. Based on the analysis of the collected data, it is known that vital signals have low signal-to-noise ratio and are characterized by quasi-periodicity and multiple harmonics; whereas clutter can be manifested as Gaussian colored noise. For this reason, this study translates the bio-radar signal processing into the problems of harmonic modeling, clutter suppression, and signal-to-noise ratio enhancement in the presence of Gaussian colored noise. The research objective is to extract the weak vital sign signals of human body from the complex background by machine learning techniques. In order to effectively extract physiological signals such as respiration and heart rate, this paper employ an ultra-wideband bio-radar system and advanced signal processing algorithms, and the experimental results show that the respiratory signal of participant 1 at 0 s is 2.1 mV, the heartbeat signal is 1.0 mV, and the suppressed clutter signal is 0.1 mV, which effectively suppresses the clutter interference with high accuracy and reliability.

关键词： Accuracy Buildings signal processing algorithms Machine learning Harmonic analysis Physiology Clutter Colored noise signal to noise ratio Ultra wideband technology

来源：评论

学校读者我要写书评

暂无评论

基于无监督图互信息最大化的海面小目标异常检测

引用

电子与信息学报 2024年第7期46卷 2712-2720页

作者：许述文何绮茹宏涛西安电子科技大学雷达信号处理国家重点实验室西安710071

受到复杂海洋环境的影响,雷达对海面慢速小目标难以实现高性能检测。对于这类目标,传统的基于能量的统计检测方法存在着严重的性能损失。针对这一问题,该文提出了基于互信息最大化框架下的海面小目标检测方法,实现海杂波背景下的无监督... 详细信息

受到复杂海洋环境的影响,雷达对海面慢速小目标难以实现高性能检测。对于这类目标,传统的基于能量的统计检测方法存在着严重的性能损失。针对这一问题,该文提出了基于互信息最大化框架下的海面小目标检测方法,实现海杂波背景下的无监督目标异常检测任务。首先,考虑到高分辨雷达回波不满足传统神经网络对样本独立同分布的假设,该文从图的角度重新建模数据,利用回波的空时相关特性来构建图拓扑结构。该文提出相对最大节点度并联合7个已有特征作为节点的初始表示向量。接下来,采用图注意力网络作为互信息最大化框架中的编码器学习节点表示向量。最后,使用异常检测算法进行目标检测,并实现虚警可控。经实测数据验证,使用快速凸包学习算法时,相比三特征检测器,所提检测器性能提升了9.2%;相比时频三特征检测器,性能提升了7.9%。当网络输出更高维的表示向量时,使用孤立森林算法的检测器的性能提升了27.4%。

关键词：海杂波目标检测图神经网络无监督学习

来源：评论

学校读者我要写书评

暂无评论

Multi-view Feature Discrepancy Attack for Single Object Tracking

Multi-view Feature Discrepancy Attack for Single Object Trac...

引用

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

作者： Zhiheng Li Weng Zhimin Yuehuan Wang Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adversarial attacks on single object tracking (SOT) have attracted increasing attention. However, most previous works have focused on adding small digital perturbations to tracking sequences, assuming access to the data, which makes these attacks impractical in real-world applications. Inspired by traditional military camouflage, we propose a texture pattern attack method with a similar implementation, called the Multi-View Feature Discrepancy Attack (MFDA). Unlike the consistent texture features of traditional camouflage, we iteratively optimize non-planar textures by obtaining gradients from the target model to enhance feature discrepancies across different viewpoints. Moreover, the model’s multi-scale features and heatmaps are utilized as targets for our feature-level and decision-level attacks, respectively. We apply texture patterns to controllable regions of vehicle models and conduct extensive attack experiments. The results show that our method significantly degrades the performance of state-of-the-art Siamese-based trackers, and also exhibits attack capability against ViT-based trackers.

关键词： Heating systems Target tracking Perturbation methods signal processing Acoustics Surface texture Object tracking Speech processing Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

On Joint Active-Passive Sonar Detection Performance of Underwater Long-Range Low-Speed small Target

On Joint Active-Passive Sonar Detection Performance of Under...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Kai Lai Xionghou Liu Yixin Yang Chao Sun School of Marine Science and Technology Northwestern Polytechnical University Xi'an P. R. Chinal Shaanxi Key Laboratory of Underwater Information Technology Xi'an China P. R. China

We discuss the feasibility of the joint active-passive sonar detection method of underwater low-speed small targets located at long ranges. Specifically, the transmission loss, the reverberation level, and the noise level related to the joint active-passive detection method are modeled and calculated. The relationship between the figure of merit and the transmission loss is used to analyze the performance of the joint active-passive detection method of underwater low-speed small targets. For obtaining a long detection range, we mainly consider the working frequency of both the active and passive sonars to be much lower than traditional small target detection sonars. The active sonar is set to be worked at a frequency band not higher than 40kHz. Meanwhile, the passive sonar is supposed to work at a frequency band not higher than 1 kHz. The factors that affect the performance of the joint active-passive detection method are analyzed in detail. The results indicate that the considered joint active-passive detection method may have a detection range of more than 2000m in a relatively good underwater acoustic environment, which provides a potential way to significantly increase the working range of the underwater detection sonar system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of Feature Information and Applying it to Substation Equipment Detection

CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Li Zhuyun Xu Chong Yang Yingyi Lai Jiayang Xian Qu Mai Xiaoming Ieiri Yuya Yoshie Osamu China Southern Power Grid Technology Co. Ltd. Guangzhou China Graduate School of Information Production and Systems Waseda University Fukuoka Japan BIWIN Storage Technology Company Limited Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Substations act as bridges connecting power gener-ation with consumption and play crucial roles in electrical grids such as power energy conversion, operational dispatching, and information monitoring. With the rapid development of electrical grids in recent years, the increasing contradiction between the shortage of production staff and the growing workload of inspections has urgently necessitated intelligent recognition algorithms for the condition of indoor substation equipment. This paper proposes a novel indoor substation equipment target detection framework based on YOLOv8, aimed at addressing issues like scale variation, occlusion handling, and real-time performance optimization, thereby enhancing detection accuracy and speed in complex environments. By incorporating the ConvNeXt feature extraction network and BiFPN, combined with the Wise-IoU loss function and adding detection heads for small targets, this approach has improved the detection capabilities for multi-scale targets, particularly in recognizing minute objects. Experimental results with actual field data indicate that the proposed CWA-YOLO model outperforms existing methods in both detection accuracy and speed, with the model mAP0.5 and mAP0.5:0.95 being 0.918 and 0.623 respectively, marking improvements of 6.50% and 11.05% over the YOLOv81 model, 7.49% and 11.85% over YOLOv71, and 9.55% and 15.80% over YOLOv51.

关键词： Substations Accuracy Target recognition signal processing algorithms Object detection Production signal processing Feature extraction data models Real-time systems

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Particle Filter Based Joint Probabilistic data Association Algorithm for Long-Duration Multi-Target Tracking in Clutter Environment

An Intelligent Particle Filter Based Joint Probabilistic Dat...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Xihao Wang Die Chen Yanru Zhao School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The Joint Probabilistic data Association Algorithm based on Particle Filter (JPDA-PF) is used in the tracking problem of nonlinear multi-target systems in dense clutter environments. However, during long time tracking, the tracking accuracy decreases with the increase of time. Meanwhile JPDA produces a combinatorial explosion in the number of associated events as the number of targets and clutter increases, greatly increasing the computational task. In this paper, a Joint Probabilistic data Association Algorithm based on dolphin swarm particle filter (JPDA-DSPF) is proposed. Using the dolphin swarm optimization algorithm as a resampling method for particle filter can optimize the particle distribution to be closer to the region of high likelihood, and enhance the particle diversity to weaken the particle degradation phenomenon. We use an adaptive sequential approach to reduce computational costs by discarding correlated events with small probabilities. Finally, it is proved by simulation experiments that the method can effectively reduce the tracking error, inhibit the increasing trend of the error with time, effectively reduce the computation task and enhance the timeliness during multi-target encounters.

关键词： Target tracking signal processing algorithms signal processing Probabilistic logic Market research Information filters Particle filters Clutter Particle swarm optimization Dolphins

来源：评论

学校读者我要写书评

暂无评论

Generative Adversarial Network for Variable-Length Sensing Waveform Synthesis 22

Generative Adversarial Network for Variable-Length Sensing W...

引用

22nd IEEE International Workshop on signal processing Advances in Wireless Communications (IEEE SPAWC)

作者： Saarinen, Vesa Koivunen, Visa Aalto Univ Espoo Finland

ISBN: (纸本)9781665428514

We propose a generative adversarial network (GAN) system for synthesizing multiple families of novel radar waveforms with desirable Ambiguity Function (AF) shapes as well as a constant modulus property. Additionally, we introduce a penalty term to promote a low cross-correlation among synthesized waveforms. Many commonly-used radar code families contain only a limited number of sequences of a certain length. In modern radar applications, such as multifunction, MIMO, and cognitive radars, this may reduce the achievable performance gains. These systems launch multiple waveforms simultaneously in order to deal with low observable targets or large numbers of small targets. Therefore, the synthesis of novel waveforms and waveform families is important. Generating new waveforms on demand is also beneficial in scenarios where an adversary attempts to detect or recognize launched waveforms. We develop a conditional Wasserstein GAN (WGAN) for multiple datasets of complex-valued input data with varying code lengths. For each dataset, the model aims to synthesize waveforms with the same length and AF shape as the waveforms in that dataset. The model is trained to synthesize two classes of waveforms with different lengths using Frank and Oppermann codes. The AF shapes of synthesized waveforms are nearly identical to those of the training data. Additionally, the proposed penalty term allows for a tradeoff between the AF fidelity of synthesized samples and the expected cross-correlation among synthesized waveforms.

关键词： Training Wireless communication Codes Shape Training data signal processing Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：