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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

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

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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

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Electroosmotic strengthening of soft clay under different electrification modes

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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

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Machine Learning-Aided Nonhomogeneity Detection Method for Airborne Radar

IEEE TRANSACTIONS ON RADAR SYSTEMS

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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)

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

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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

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

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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

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Shape Parameter Estimation of K-Distributed Sea Clutter Using Neural Network and Multisample Percentile in Radar Industry

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第6期19卷 7602-7612页

作者： xue, Jian Sun, Mengling Liu, Jun xu, shuwen Pan, Meiyan Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710122 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230052 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xian Elect Engn Res Inst Xian 710049 Peoples R China

In this paper, we consider the problem of robustly and accurately estimating the shape parameters of K-distributed sea clutter in the maritime radar industry. Outliers formed by non-sea-surface echoes have a significant negative impact on the estimation accuracy. To improve the estimation performance, we first propose a bipercentiles feedforward neural network for the shape parameter eta (BPFFNN-eta), which utilizes a ratio of two percentiles and a twohidden-layer feedforward neural network. The BP-FFNN-eta can learn the mathematical relationship between the shape parameter and the ratio of two percentiles, and can work in environments where the number of outliers is approximately known. Moreover, to solve the case where the number of outliers is not known due to dynamic changes in the environment, we also design another neural network (referred to as MBP-FFNN-eta), which consists of multiple BP-FFNNs-eta and a multi-class classification network. The MBP-FFNN-eta can perceive the change in the proportion of outliers, so an accurate estimate can be obtained from an unaffected BP-FFNN-eta. Finally, training and test data are constructed to train and evaluate the proposed methods, respectively. Experimental results demonstrate that the BPFFNN-eta performs better than traditional moments-based estimators, and has almost the same performance as the tri-percentile estimator. Compared with the tri-percentile estimator, the BP-FFNN-eta avoids table lookups, and produces a continuous estimate. The MBP-FFNN-eta can achieve more than 97% overall classification accuracy on simulated and measured data, and thus an accurate estimate of the shape parameter can be obtained when the number of outliers varies.

关键词： K distribution neural network sample percentiles sea clutter shape parameter estimation

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Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

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PHYSICS IN MEDICINE AND BIOLOGY 2024年第5期69卷 055022-055022页

作者： Hu, Qiu Cai, Weiming xu, shuwen Hu, Shaohai Wang, Lang He, Xinyi NingboTech Univ Sch Informat Sci & Engn Ningbo 315100 Peoples R China Zhejiang Engn Res Ctr Intelligent Marine Ranch Eq Ningbo 315100 Peoples R China China Elect Technol Grp Corp Res Inst 3 Beijing 100846 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Ningbo Xiaoshi High Sch Ningbo 315100 Peoples R China

Objective. Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF. Approach. The fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain's sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction. Main results. Experimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations. Significance. The proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity

关键词： multimodal medical image image fusion sparse representation convolutional sparse representation non-subsampled contourlet transform misregistration

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Adaptive Bayesian Detection for Multistatic Radar in Compound-Gaussian Clutter with Inverse Gaussian Texture 15

Adaptive Bayesian Detection for Multistatic Radar in Compoun...

引用

15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Zhou, Hao xu, shuwen Cui, Yaosheng National Key Laboratory of Radar Signal Processing Xidian University No. 2 South Taibai Road Shaanxi Xi’an710071 China

ISBN: (纸本)9781510675056

This work addresses the problem of adaptive target detection in compound-Gaussian clutter for multistatic radar. We exploit a-priori knowledge of the clutter to alleviate the performance degradation due to the non-Gaussian characteristic of the clutter. Here, the inverse Gaussian distribution is adopted to describe the texture of the clutter. In addition, the speckle covariance matrix of the clutter is modeled as a random matrix following the complex inverse Wishart distribution. Using different fusion rules between the a-priori knowledge of the texture and the speckle component within the Bayesian framework, two adaptive detectors are derived based on the Generalized Likelihood Ratio Test (GLRT) criterion. Finally, the detection performance of the proposed detectors is verified using simulated data. The results show the superiority of the proposed adaptive detectors over the existing techniques. © 2024 SPIE.

关键词： Textures

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Floating Small Target Detection in Sea Clutter Based on Multifeature Angle Variance

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 9849-9863页

作者： Bai, Xiaohui xu, shuwen Zhu, Jianan Guo, Zixun Shui, Penglang Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Shenyang Aircraft Design & Res Inst AVIC Shenyang 110000 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Amid radar target detection fields, the detection of small floating targets in sea clutter is a tough but crucial task. An efficient solution to address the issue is feature-based detection method based on distinguishable features extracted from sea clutter and returns with target. Nevertheless, as the number of features increases, traditional algorithms with dimension limitation are not adaptable to combine the multiple features to detect. In this article, angle variance-based anomaly detection algorithms which solve the problem of the "curse of dimensionality" are introduced into floating small target detection. An improved algorithm by using the Mahalanobis distance is proposed and for the purpose of detecting the sea-surface small target, a novel detector with controlled false alarm capability is developed. On the open and recognized databases, the detection abilities of the proposed detector are assessed. It is confirmed that the proposed multifeature angle variance-based detector is more effective than other detectors based on multifeature distance or density from experimental results. This proposed detector can implement the improvement of detection performance with less computational cost compared to other well-established detectors.

关键词： Feature extraction Detectors Clutter Radar Radar detection Object detection Radar cross-sections Angle variance anomaly detection feature-based detection floating small target detection Sea clutter

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