检索结果-内蒙古大学图书馆

Classification of Small Targets on Sea Surface Based on Improved Residual Fusion Network and Complex Time-Frequency Spectra

引用

REMOTE SENSING 2024年第18期16卷 3387-3387页

作者： xu, shuwen Niu, Xiaoqing Ru, Hongtao Chen, Xiaolong Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Naval Aviat Univ Yantai 264001 Peoples R China

To address the problem that conventional neural networks trained on radar echo data cannot handle the phase of the echoes, resulting in insufficient information utilization and limited performance in detection and classification, we extend neural networks from the real-valued neural networks to the complex-valued neural networks, presenting a novel algorithm for classifying small sea surface targets. The proposed algorithm leverages an improved residual fusion network and complex time-frequency spectra. Specifically, we augment the Deep Residual Network-50 (ResNet50) with a spatial pyramid pooling (SPP) module to fuse feature maps from different receptive fields. Additionally, we enhance the feature extraction and fusion capabilities by replacing the conventional residual block layer with a multi-branch residual fusion (MBRF) module. Furthermore, we construct a complex time-frequency spectrum dataset based on radar echo data from four different types of sea surface targets. We employ a complex-valued improved residual fusion network for learning and training, ultimately yielding the result of small target classification. By incorporating both the real and imaginary parts of the echoes, the proposed complex-valued improved residual fusion network has the potential to extract more comprehensive features and enhance classification performance. Experimental results demonstrate that the proposed method achieves superior classification performance across various evaluation metrics.

关键词： target classification residual network complex neural networks time-frequency spectra

来源：评论

学校读者我要写书评

暂无评论

Compound Gaussian Clutter Modelling with Log-Laplace Distributed Texture and Parameter Estimation Using Fractional-Moment-Based Method 16

Compound Gaussian Clutter Modelling with Log-Laplace Distrib...

引用

16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhou, Hao xu, shuwen National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

This paper deals with the problem of modelling the amplitude distribution of sea clutter for low-grazing-angle and high-resolution radar systems. Since the compound Gaussian model has been widely used for modelling the non-Gaussian clutter, we propose a compound Gaussian model with log-Laplace distributed texture for amplitude distribution modelling of measured sea clutter data. The expressions for the amplitude probability density function and cumulative distribution function are given. We also derive estimation methods using log-cumulants and fractional order moments to estimate the distribution parameters. Several real sea clutter datasets are used for amplitude distribution analysis and model fits. The results of the model fits show that the proposed compound Gaussian model provides better fits to the data than common compound Gaussian models. © 2025 SPIE.

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

A Novel Method for Small-Target Detection in Sea Clutter: Spectral Clustering Based on Neighborhood Density Similarity Measure

引用

IEEE SENSORS JOURNAL 2025年第2期25卷 2988-2997页

作者： Zhang, Le Wang, Qingfei Guo, Yunfei xu, shuwen Wang, Lin Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Yancheng Inst Technol Sch Elect Engn Yancheng 224051 Peoples R China

Small-target detection in sea clutter has always been difficult due to the low signal clutter ratio (SCR). Feature-based detection methods have emerged as a significant area of research in recent years, where pattern recognition methods involve the use of similarity measures. The Gaussian kernel function used in traditional spectral clustering algorithms performs well when handling nonlinear data, however, it may not effectively capture the relationships between sparsely distributed samples. Therefore, this article proposes a novel method for small-target detection in sea clutter: spectral clustering based on neighborhood density similarity measure (SC-ND) to address the aforementioned issues. It incorporates neighborhood density into the Gaussian kernel function and then uses label information to guide the construction of the similarity matrix, at which point a false alarm controllable is achieved. The similarity matrix is then spectrally decomposed to obtain a new representation of the samples, which are subsequently classified using an improved k-means algorithm. Validated on the IPIX 1993 radar dataset, the proposed method achieves a detection probability of 71.1% with an observation time of 1.024 s and a false alarm rate of 0.001. The results are better than fractal-based detector, tri-feature-based detector, and feature-based detector using three TF features.

关键词： Sea measurements Clutter Feature extraction Detectors Clustering algorithms Object detection Kernel Databases Time-frequency analysis Radar clutter False alarm controllable label information neighborhood density small-target detection spectral clustering

来源：评论

学校读者我要写书评

暂无评论

Electroosmotic strengthening of soft clay under different electrification modes

引用

ENERGY SCIENCE & ENGINEERING 2024年第7期12卷 3126-3141页

作者： Sun, Zhaohua xu, shuwen Zhang, Cheng Geng, Jingxian Gu, Yefeng Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China Chongqing Univ Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing Peoples R China

Electrification mode is one of the important factors affecting the energy consumption and drainage effect of the electroosmosis method. In this work, a series of laboratory tests were performed to explore the impact of continuous changes in the electric field on the migration of ions and electrons, as well as the migration of electroosmotic flow. The results show that different electrification modes had a significant impact on the current and water discharge. An electrode reversed from anode to cathode, from one circuit to another, is beneficial to drainage, while an electrode reversed from cathode to anode, from one circuit to another, is unfavorable to drainage. The drainage effect of the specific cyclic and progressive electroosmosis (CPE) is not as good as the conventional electroosmosis method. However, combining the two electrification modes can further enhance the shear strength of the soil near a designated electrode. The generation of transverse cracks in soil is mainly due to the drainage difference caused by the electroosmotic flow, which is caused by the adjacent electric field at that location. When the electroosmotic flow is strong, longitudinal cracks will occur in the soil along the strongest electroosmotic flow path. In practical in situ engineering applications, the electrification mode of CPE will be more energy-efficient than conventional electrification modes with the same processing time. Electroosmotic flow migration, under different electrification basic modes, in soft clay. image

关键词： CPE electric field electrification mode electroosmosis soft clay

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Aided Nonhomogeneity Detection Method for Airborne Radar

IEEE TRANSACTIONS ON RADAR SYSTEMS

引用

IEEE TRANSACTIONS ON RADAR SYSTEMS 2025年 3卷 220-232页

作者： Wang, Zeyu Chen, Hongmeng xu, shuwen Li, Ming Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

The weight vector in space-time adaptive processing (STAP) algorithm will lead to notches at the position of the interfering targets when there are interfering targets in the training data. If these interfering targets are close to the target of interest on the space-time spectrum, the target signal self-nulling occurs. To deal with this problem, a machine learning-aided nonhomogeneity detection (ML-NHD) method is proposed. More specifically, the subaperture smoothing technique is first performed on each training data to obtain the subaperture sample covariance matrices (SCMs). We prove that when the airborne radar works in side-looking mode and the clutter foldover factor is an integer, the numbers of large eigenvalues (EIGs) of the subaperture SCMs are different for the ordinary training data samples and outlier training data samples. Then, four features are constructed based on the differences in the characteristics of EIGs and eigenvectors of the subaperture SCMs. Finally, a binary classifier based on support vector machine (SVM) is trained to classify the ordinary training data and the outlier training data. The performance assessment shows that the ML-NHD method can detect the outlier training data effectively and achieves better performance of clutter suppression compared with the conventional methods.

关键词： Training data Clutter Covariance matrices Airborne radar Vectors Eigenvalues and eigenfunctions Radar detection Training Radar clutter Machine learning algorithms machine learning nonhomogeneity detection space-time adaptive processing (STAP)

来源：评论

学校读者我要写书评

暂无评论

Marine Small Floating Target Detection Method Based on Fusion Weight and Graph Dynamic Attention Mechanism

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1-11页

作者： Ru, Hongtao xu, shuwen He, Qi Shui, Penglang Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Understa Xian 710071 Peoples R China Mcmaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

Sea surface target detection with graph neural networks (GNNs) is an emerging method. However, the correlation information of radar returns cannot be efficiently exploited by the conventional graph convolutional network (GCN). Therefore, this article proposes a small floating target detection method based on a graph attention network (GAT) with spatiotemporal correlation of clutter maps, and designs fusion weighting and dynamic attention mechanism for practical problems. First, the dwell radar data are modeled as a graph structure according to its spatio-temporal information. The proposed graph structure allows Doppler spectra of same-type samples to be accumulated separately for sea clutter and target returns. Then, we propose a GAT-based detector and optimize it to create variants: GATv2, GAT-Fused, and GATv2-Fused. These variants aim to reduce sea spike interference and jointly utilize spatiotemporal clutter map information and feature correlation. Both measured and simulated data demonstrate that the proposed attention-based detectors effectively identify marine small floating targets, including out-of-distribution (OOD) detection, outperforming conventional feature-based detectors, the GCN detector, and the pure GAT detector.

关键词： Feature extraction Clutter Radar Detectors Convolution Graph neural networks Correlation Clutter map graph attention network (GAT) graph structural target detection

来源：评论

学校读者我要写书评

暂无评论

Image fusion based on discrete Chebyshev moments?

引用

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2023年第1期92卷

作者： Chen, Xiaoxuan xu, shuwen Hu, Shaohai Ma, Xiaole Network Technol Beijing Key Lab Adv Informat Sci Beijing 100000 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100000 Peoples R China Res Inst TV & Electroacoust Beijing 100000 Peoples R China

Though deep learning-based methods have demonstrated strong capabilities on image fusion, they usually improve the fusion performance by increasing the width and depth of the network, increasing the compu-tational effort and being unsuitable for industrial applications. In this paper, an end-to-end network based on fixed convolution module of discrete Chebyshev moments is proposed, which does not need any pre-or post-processing. The proposed network is roughly composed of three parts: feature extraction module, fusion module and feature reconstruction module. In the feature extraction module, a novel fixed convolution module based on discrete Chebyshev moments is proposed to obtain different frequency components in a short time. To improve the image sharpness and fuse more details, a spatial attention mechanism based on average gradient is proposed in fusion module. Extensive results demonstrate that the proposed network can achieve remarkable fusion performance, high time efficiency and strong generalization ability.

关键词： Image fusion Attention mechanism Chebyshev moments Average gradient

来源：评论

学校读者我要写书评

暂无评论

Consecutive Pruning Enhances Leaf Flavonoids, Leaf Yield, and Cutting Rooting in Ginkgo biloba

引用

FORESTS 2024年第5期15卷 761-761页

作者： Zhong, Lei xu, Shiyuan xu, shuwen Zhou, Wanxiang Lu, Zhaogeng Jin, Biao Wang, Li Yangzhou Univ Coll Hort & Landscape Yangzhou 225009 Peoples R China

Ginkgo biloba L. is a valuable medicinal plant known for its high content of flavonoids and terpenoids in the leaves of young trees. Pruning can increase leaf yield in ginkgo plantations;however, it is unclear how the intensity of pruning affects leaf yield and quality. In addition, G. biloba exhibits low cutting rooting rates, which limits its efficiency in asexual propagation. In our study, we compared consecutive pruning with varying levels of intensity, including top pruning, light pruning, and heavy pruning, to evaluate the effects of pruning on leaf yield and cutting rooting. The results showed that these three pruning methods all contributed to an increase in the number of new branches, the leaf weight, and the flavonoid content in five-year-old trees. Among them, the effect of light pruning was the best, with a 150% increase in branch number, a 130% increase in leaf weight, and a 40.6% increase in flavonoid content. The secondary pruning further increased leaf area by 22.3%, indicating that secondary pruning further enhanced the rejuvenation of plants and increased leaf yield. At the transcriptional level, pruning can significantly change the expression of genes related to bud sprouting, resulting in a particularly significant increase in SHR expression in the buds. Pruning also promoted the expression of important genes related to flavonoid synthesis, including chalcone synthase (CHS), flavonoid 3 '-hydroxylase (F3 ' H), flavonol synthase (FLS), and dihydroflavonol reductase (DFR). Furthermore, we demonstrated a significant increase in the rooting rate of these second-pruned branch cuttings and screened the optimal hormone ratio for rooting, which is 1.5 mu M MeJA + 400 mg/L NAA + 100 mg/L Uniconazole-P. These results suggest that secondary pruning can effectively rejuvenate plants to promote cutting rooting in G. biloba. This method can not only be used to improve the yield and quality of ginkgo leaves, but also for cutting propagation.

关键词： pruning intensity cutting rooting gene expression flavonoid compounds plant regeneration

来源：评论

学校读者我要写书评

暂无评论

Persymmetric Adaptive Radar Target Detection in CG-LN Sea Clutter Using Complex Parameter Suboptimum Tests

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： xue, Jian Fan, Zhen xu, shuwen Liu, Jun Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Xian Univ Posts & Telecommun Sch Artificial Intelligence Xian 710121 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Understa Xian Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China

In this article, we consider the detection problem of marine radar targets embedded in correlated non-Gaussian sea clutter, which is modeled by a compound Gaussian model with lognormal texture (CG-LN) and unknown covariance matrices. To reduce the dependence of detectors on training data, the original radar data are transformed via exploiting the persymmetric structure of clutter covariance matrix. Then, four complex parameter suboptimum tests, which are the Rao, Wald, Gradient, and Durbin tests, are used to design the adaptive persymmetric coherent detectors for radar targets. It is shown that the Gradient test and the Durbin test coincide with the Rao test for the problem of radar target detection in CG-LN sea clutter. We prove that the proposed persymmetric Rao detector with lognormal texture (PRAO-LND) and persymmetric WALD detector with lognormal texture (PWALD-LND) can ensure the constant false alarm rate (CFAR) with respect to the clutter power mean and the clutter speckle covariance matrix. Experimental results on simulated and measured radar data show that the proposed PRAO-LND performs better than its competitors and is robust to the mismatched signals. Moreover, the proposed PWALD-LND has asymptotic performance with the proposed PRAO-LND, when the non-Gaussianity of sea clutter is weakened, and has good selectivity with signal mismatch.

关键词： Complex parameter suboptimum test non-Gaussian sea clutter persymmetric structure target detection

来源：评论

学校读者我要写书评

暂无评论

Maritime Target Radar Detection and Tracking via DTNet Transfer Learning Using Multi-Frame Images

引用

REMOTE SENSING 2025年第5期17卷 836-836页

作者： He, Xiaoyang Chen, Xiaolong Du, Xiaolin Wang, Xinghai xu, shuwen Guan, Jian Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Naval Aviat Univ Marine Target Detect Res Grp Yantai 264001 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Traditional detection and tracking methods struggle with the complex and dynamic maritime environment due to their poor generalization capabilities. To address this, this paper improves the YOLOv5 network by integrating Transformer and a Convolutional Block Attention Module (CBAM) with the multi-frame image information obtained from radar scans. It proposes a detection and tracking method based on the Detection Tracking Network (DTNet), which leverages transfer learning and the DeepSORT tracking algorithm, enhancing the detection capabilities of the model across various maritime environments. First, radar echoes are preprocessed to create a dataset of Plan Position Indicator (PPI) images for different marine conditions. An integrated network for detecting and tracking maritime targets is then designed, utilizing the feature differences between moving targets and sea clutter, along with the coherence of inter-frame information for moving targets, to achieve multi-target detection and tracking. The proposed method was validated on real maritime targets, achieving a precision of 99.06%, which is a 7.36 percentage point improvement over the original YOLOv5, demonstrating superior detection and tracking performance. Additionally, the impact of maritime regions and weather conditions is discussed, showing that, when transferring from Region I to Regions II and III, the precision reached 92.2% and 89%, respectively, and, when facing rainy weather, although there was interference from the sea clutter and rain clutter, the precision was still able to reach 82.4%, indicating strong generalization capabilities compared to the original YOLOv5 network.

关键词： maritime moving targets detection and tracking radar images deep learning inter-frame information

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：