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16th International Conference on signal processing Systems, ICSPS 2024

ISBN: (纸本)9781510689251

The proceedings contain 197 papers. The topics discussed include: variational modal decomposition based on whale optimization algorithm for vital signs detection of FMCW radar;sea surface small target detection using tri-median normalized doppler spectrum feature;a physical cluster-based passive scatterer detection method in ISAC systems;on detection of moving weak targets using MIMO continuous active sonar and Doppler filtering;design of optical testing turntable data acquisition system based on LabVIEW;AOA /TDOA location technology of weak satellite signal based on satellite-ground cooperation;bearing-crossing localization technology for UAV target based on signal characteristics;and separation and localization of multiple bat signals using frequency domain beamforming and dual-array-localization method.

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An Efficient Coherent Integration Method for Highly Maneuvering Target Detection by Exploiting Range-Azimuth Coupling Relationships Between Subsegments

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IEEE Sensors Journal 2025年第9期25卷 15456-15476页

作者： Wan, Jun Jiang, Xinyu Liu, Chengyuan Li, Dong Xie, Xiaoran Sang, Qinglin Chen, Zhanye Chongqing University School of Microelectronics and Communication Engineering Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion Chongqing400044 China China Academy of Electronics and Information Technology Beijing100041 China State Key Laboratory of Millimeter Waves Institute of Electromagnetic Space Southeast University Nanjing210096 China

Arbitrary-order range migrations (RMs) and Doppler frequency migrations (DFMs) caused by complicated motions of highly maneuvering targets (HMTs) pose challenges for coherent integration (CI) in radar remote sensing and detection applications. Complicated Doppler frequency ambiguity improves the challenges of CI for HMTs. The optimal generalized Radon-Fourier transform (GRFT) suffers from a large computational cost, and nonlinear transform-based methods are affected by severe performance loss. To tackle these difficulties, an efficient segmented CI method for HMT detection via the range-azimuth coupling relationship between subsegments is proposed. First, inspired by the advantage of subaperture processing, a segmentation operation is introduced to remove intrasegment RMs and DFMs. Second, a high-order compensation function is presented to remove the high-order intersegment RMs and DFMs. Third, by exploiting the range-azimuth coupling relationship between subsegments, a residual migration compensation function is used to correct the residual intersegment RM. Thus, a well result of HMT's CI is achieved. The proposed method handles arbitrary-order RMs and DFMs, complex Doppler ambiguity, and ambiguous speed sidelobes (BSSLs) in low signal-to-noise ratio (SNR) circumstances and multitarget cases due to its linear transform property. In comparison to the optimal GRFT, the proposed approach substantially lowers computational cost while showing similar performance. Then, the presented approach can be implemented by parallel processing. It is advantageous to enhance real-time processing efficiency. Several real radar data are analyzed to confirm the effectiveness of the developed method. © 2001-2012 IEEE.

关键词： signal to noise ratio

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Detection Framework of FDIA for Smart Grid Based on Mode Decomposition and Gradient Boosting Decision

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年 1-23页

作者： Zhang, Jing Zhou, Kun Wang, Lulu Yang, Yang Li, Yingna Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Prov Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Prov Key Lab Comp Technol Applicat Kunming 650500 Yunnan Peoples R China

The escalating frequency of cyberattacks poses a significant threat to the security of Cyber-Physical Systems. Recent studies have revealed that a specific type of cyberattack, known as false data injection attacks(FDIA), can easily breach the defense mechanisms of energy systems. As attack detection technology improves, attackers must prudently choose their targets and distribute discreetly small-scale resources to minimize the risk of detection. Without access to eliminate the strong coupling relationship between buses within single time domain, we propose a FDIA detection framework that can detect independent buses, which presents the characteristics of attack under the multi-mode perspective. The framework employs Variational Mode Decomposition and Fast Independent Component Analysis to extract the distinctive features of FDIA signals across various frequencies. Additionally, the framework utilizes the results obtained from Particle Swarm Optimization to replace the predetermined thresholds of Extreme Gradient Boosting on an individual bus, thereby augmenting both the detection accuracy and interpretability of the model. Simulation results of the IEEE-14 bus system demonstrate the efficient detection of attacks on individual buses by the proposed FDIA detection framework. Furthermore, the study evaluates the impact of attack intensity on the framework's performance in the presence of specific noise interference, thus confirming the method's high effectiveness and robustness.

关键词： Cyber-physical system security signal processing False data injection attacks Smart grid Machine learning

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Learning adaptive detection and tracking collaborations with augmented UAV synthesis for accurate anti-UAV system

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Liu, Shihan Xu, Tianyang Zhu, Xue-Feng Wu, Xiao-Jun Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford GU2 7XH England

In recent years, Unmanned Aerial Vehicles (UAVs) have witnessed a significant use upsurge across various domains. However, their low cost and elusive size have raised significant security concerns. In particular, UAVs, imaged in the infrared mode, located in a cluttered background and being relatively small targets, pose formidable challenges. To this end, in this paper, we propose a novel approach for accurate UAVs perception. The core innovation lies in learning an adaptive detection and tracking collaboration mechanism, supported by a novel method of training data augmentation (ADTC). During the test phase, ADTC begins by leveraging the detector to identify the potential target candidates within the image frame. These candidates are then refined by an adaptive selection module, where a Kalman filter is deployed to model and predict the motion trajectory of each candidate. The best result of the predicted trajectories with the detected candidates is adopted as the output. The adaptive selection module filters out less confident objects, efficiently decreasing processing time. Furthermore, we construct a new dataset Anti-MUAV15 to evaluate the performance of ADTC in multipleUAV scenarios. Our approach has been rigorously evaluated through qualitative and quantitative experiments on the Anti-UAV, AntiUAV600 and Anti-MUAV15 datasets. The experimental results demonstrate that our method outperforms state-of-the-art anti-UAV solutions in terms of robustness and precision, without imposing additional computational burden. The code and dataset are available at https://***/Shihan0325/AntiMUAV15.

关键词： Anti-UAV system Object detection Visual tracking data augmentation

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

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16th International Conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

This letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). The median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. The three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. Through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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Hybrid Deterministic Sensing Matrix for Compressed Drone SAR Imaging and Efficient Reconstruction of Subsurface targets

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REMOTE SENSING 2025年第4期17卷

作者： Jo, Hwi-Jeong Lee, Heewoo Choi, Jihoon Lee, Wookyung Korea Aerosp Univ Sch Elect & Informat Engn Goyang 10540 Gyeonggi Do South Korea

Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments.

关键词： synthetic aperture radar (SAR) subsurface target drone compressive sensing

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Transcriptome analysis and functional identification of transfer RNA-derived fragments in grape leaves exposed to UV-C radiation

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PLANT PHYSIOLOGY AND BIOCHEMISTRY 2025年 219卷 109425页

作者： Li, Yang Kong, Lingchao Mu, Huayuan Wang, Jiayu Li, Furui Kuang, Yangfu Duan, Wei Fan, Peige Yuan, Ling Liang, Zhenchang Wang, Lijun Chinese Acad Sci Inst Bot Beijing Key Lab Grape Sci & Enol Beijing 100093 Peoples R China Chinese Acad Sci Inst Bot State Key Lab Plant Divers & Specilaty Crops Beijing 100093 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Natl Bot Garden Beijing 100093 Peoples R China Univ Kentucky Kentucky Tobacco Res & Dev Ctr Dept Plant & Soil Sci Lexington KY 40546 USA

Transfer RNA-derived fragments (tRFs) are noncoding small RNAs derived from transfer RNAs (tRNAs) in microorganisms, animals and plants. In plants, tRFs are known to respond to environmental stimuli, including heat, oxidative stress and UV radiation;however, their specific functions in horticultural plants, such as grapevine, remain poorly understood. In this study, we used RNA-seq to identify differentially expressed genes (DEGs) in grape leaves exposed to UV-C radiation. A total of 1329 and 8055 of genes were differentially expression after 1 and 6 h of UV-C treatment, respectively. We identified a large number of secondary metabolism-related genes in the DEGs, including genes involved in stilbene and flavonoid biosynthesis. Noticeably, the stilbene biosynthesisrelated gene was induced earlier than the other genes in the phenylalanine metabolic pathway. We also conducted small RNA-seq and identified differentially expressed (DE) miRNAs and their targets. To explore whether the tRFs involved in UV-C response, further analysis of the small RNA-seq data revealed 23 down-regulated and 41 up-regulated DE tRFs. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that the target genes of these tRFs are involved in multiple biological processing, including hormone signal transduction and metabolite synthesis. To validate the function of tRFs, tRF39 and tRF45 were selected and overexpressed in tobacco leaves, and the expression levels of their target genes were inhibited. Our study suggests that the tRFs may regulate multiple biological processes in response to UV-C exposure in grapevine. Our findings provide a foundation for further elucidating the regulatory mechanisms of tRFs in horticultural crops.

关键词： small RNA tRNA fragments UV-C radiation Phenylalanine metabolic pathway Grapevine

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FBI-Net: Frequency Band Integration Network for Infrared small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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

作者： Biqiao Xin Qiang Li Qianchen Mao Jinbao Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches.

关键词： Wavelet transforms Frequency-domain analysis Semantics signal processing Feature extraction Excavation Robustness Real-time systems Complexity theory Speech processing

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基于多域雷达回波数据融合的海面小目标分类网络模型

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电子与信息学报 2025年第3期47卷 696-706页

作者：赵子健许述文水鹏朗西安电子科技大学雷达信号处理全国重点实验室西安710071

海面小目标识别是海事雷达监视任务中一个重要且具有挑战性的问题。由于海面小目标类型多样、环境复杂多变,对其进行有效分类存在较大困难。在高分辨体制雷达下,海面小目标通常只占据一或几个距离单元,缺乏足够的空间散射结构信息,因此... 详细信息

海面小目标识别是海事雷达监视任务中一个重要且具有挑战性的问题。由于海面小目标类型多样、环境复杂多变,对其进行有效分类存在较大困难。在高分辨体制雷达下,海面小目标通常只占据一或几个距离单元,缺乏足够的空间散射结构信息,因此目标的雷达截面积(RCS)起伏和径向速度变化成为分类的主要依据。为此,该文提出一种基于多域雷达回波数据融合的分类网络模型,用于海面小目标的分类任务。由于不同域的数据具有其特殊的物理意义,因此该文构建了时域LeNet(T-LeNet)神经网络模块和时频特征提取神经网络模块,分别从雷达海面回波信号的幅度序列和时频分布(TFD)即时频图中提取特征。其中幅度序列主要反映了目标RCS的起伏特性,而时频图不仅反映RCS起伏特性,还能体现目标径向速度的变化。最后,利用IPIX,CSIR数据库和自测的无人机数据集构建了包括4种海面小目标的数据集:锚定漂浮小球、漂浮船只、低空无人机(UAV)和移动的快艇。实验结果表明所提方法具有良好的识别能力。

关键词：海面小目标目标分类多特征融合神经网络

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A Reinforcement Learning Agent Controlled Multi-branch small Object Detection Framework

A Reinforcement Learning Agent Controlled Multi-branch Small...

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

作者： Junkun Hong Yitian Long Yueyi Luo Liujie Hua Jun Long Qianqian Qi School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University Nashville Tennessee United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The past few years have witnessed the immense development of small object detection, which is aimed at detecting size-limited targets in high-resolution images. The prevailing methods focus on extracting fine-grained information by expanding the receptive fields and then generating the potential small object region. However, these solutions inevitably add sophisticated detectors and extra learning components, which incur time-consuming and computation-costing. Meanwhile, we observe that it’s suboptimal to extract fine features in such a generic way. To alleviate the issues, we propose a multi-branch small object detection framework with a regular-scale detection branch and a small-scale detection branch. Specifically, we design and pre-train a reinforcement learning agent to control feature extractors in both branches according to the results of small object areas. Moreover, we present a region clipping algorithm to rebuild the small object to regular size, which can be input into a mature detector directly. The extensive experiments on COCO, VisDrone, SODA-D, and our collecting TVDS datasets demonstrate our method outperforms the state-of-the-art methods in several metrics.

关键词： Measurement Pipelines signal processing algorithms Object detection Reinforcement learning Detectors signal processing Feature extraction data mining Speech processing

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