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GLS: A hybrid deep learning model for radar emitter signal sorting

DIGITAL signal processing 2025年 161卷

作者： Qi, Liangang Chen, Hongzhuo Guo, Qiang Huang, Shuai Kaliuzhnyi, Mykola Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China Kharkiv Natl Univ Radio Elect Sci & Res Lab UA-61166 Kharkiv Ukraine

Radar emitter signal sorting is a pivotal aspect of radar reconnaissance signal processing. The increasing density of the electromagnetic environment in modern radar pulse streams, coupled with the growing complexity and variability of operational modes and signal forms, results in extremely limited reference data. Consequently, most existing sorting methods fall short of meeting the performance requirements of modern electronic warfare. To enhance sorting performance under conditions of limited samples and labeled data, this paper proposes a radar emitter signal sorting model based on ResGCN-BiLSTM-SE (GLS). Firstly, we propose a novel adaptive weighted adjacency matrix construction method that aggregates multi-scale information of local and global features. Based on this, for GLS networks, the graph convolutional network (ResGCN) is combined with the bidirectional long short-term memory (BiLSTM) network. The GCN is employed to extract attribute features from interleaved radar pulse sequences, while the BiLSTM is utilized to deeply capture the temporal dependence in interleaved pulse sequences after feature extraction. Finally, an improved squeeze-and-excitation (SE) module is applied to perform weighted fusion of critical channel information from both spatial and temporal features. Simulation results demonstrate that the proposed method not only achieves higher accuracy under small sample conditions compared to existing methods, but also exhibits strong robustness in challenging scenarios involving measurement errors, missing pulses, and spurious pulses.

关键词： Radar emitter signal sorting (RESS) Adaptive weighted adjacency matrix GLS model Features fusion

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Detection of small objects in remote sensing images based on bi-level routing attention and deformable convolution

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DIGITAL signal processing 2025年 160卷

作者： Chen, Pengbing Liu, Shouxin Feng, Wenshan Wang, Hui Li, Xiaowei Sichuan Univ Sch Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China

Remote sensing images can offer detailed and abundant data regarding geographical surface features. However, existing object detection algorithms still suffer from problems such as susceptibility to complex background interference and loss of information about small objects. To address these challenges, we propose a Deformable convolution and Bi-level routing attention reinforced YOLO (DB-YOLO) algorithm. Firstly, Bi-level Routing Attention (BRA) is introduced to enhance the feature extraction capability. Following this, Deformable ConvNets v2 (DCNv2) is utilized to substitute part of traditional convolution, mitigating the loss of small object features. Finally, the Normalized Wasserstein Distance-Complete Intersection over Union (NWD-CIoU) loss, which is insensitive to the object scale, is used for localization loss. Through the experimental verification on two public remote sensing datasets, our method can improve the detection ability of small objects. Specifically, the mAP@0.5 achieves 94.8% and 97.8% on NWPU VHR-10 and RSOD, respectively.

关键词： Remote sensing images small object detection Bi-level routing attention Deformable convolution

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Robust Range Super-Resolution Imaging With Multicriteria Joint Constraints via Double Smoothed l0-Norm Under Limited Resources

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 5145-5165页

作者： Liu, Zhexian Shao, Shuai Liu, Hongwei Su, Tao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The radar's high-resolution range profile (HRRP) contains rich physical structural features of targets, making it of great value for target recognition and classification. However, in practical applications, the effective bandwidth of radar signals is limited due to the nonideal factors such as spectrum control, limited hardware equipment capabilities, and spectrum electromagnetic interference. Thus, it is difficult to reach the requirement of high-resolution and accurate target characterization in range profiles under limited resources, which will reduce subsequent detection, imaging, and recognition performance. To address the problem, this article proposes a robust range profile super-resolution method with multicriteria joint constraints of sparsity, low-rank, and Doppler under a multiple measurement vector model. In this technique, based on compressed sensing theory and adaptive filtering framework, a sparse reconstruction algorithm via double smoothed l(0)-norm is proposed, which constructs a continuously differentiable Gaussian class function to approximate the l(0)-norm. It combines accelerated gradient algorithm and zero attractor to optimize the multicriteria cost function and iteratively reconstruct the super-resolution range profile. The algorithm improves the super-resolution performance of range profiles, enhances the robustness against noise and complex range profile configurations, and can obtain high-precision HRRP of targets with limited radar resources. Extensive simulation and real data experiments demonstrate that it not only improves the average reconstruction accuracy of super-resolution range profile sequence, but effectively reduces the fluctuation of reconstruction errors. In addition, it does not require high azimuth resolution, and is also highly effective with a small number of sequence echoes.

关键词： Image reconstruction Superresolution Radar Accuracy Target recognition signal processing algorithms Scattering Radar imaging Vectors Doppler effect Compressed sensing (CS) double smoothed l(0) -norm (DSL0) high-resolution range profile (HRRP) limited resource multicriteria joint constraints super-resolution imaging

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Research on ZYNQ neural network acceleration method for aluminum surface microdefects

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DIGITAL signal processing 2025年 157卷

作者： Zhao, Dongxue Liu, Shenbo Zhang, Zhigang Zhang, Zhao Tang, Lijun Changsha Univ Sci & Technol Sch Phys & Elect Sci Changsha 410114 Hunan Peoples R China

Convolutional Neural Networks (CNN) are an important means of detection of microdefects on the aluminum surface, and the high complexity and computing power requirements of the CNN model lead to difficulties in deploying them on edge computing platforms as the detection accuracy continues to improve. We have studied a lightweight acceleration method for detecting microdefects on aluminum surfaces on the Zynq-7000 All Programmable SoC (ZYNQ) platform. A lightweight aluminum surface defect detection network (LADFastDet) and high-performance accelerators based on ZYNQ are designed to meet the requirements of precision and speed under limited resources. In the LADFastDet structure, a lightweight inverted residual block is designed by combining depthwise convolution, inverted residual block, and inverted bottleneck. A multiscale feature fusion structure is designed to effectively improve the detection accuracy of LADFastDet, especially small target defects. We design accelerators on ZYNQ through optimization methods such as loop optimization strategy, ping-pong buffering, and multichannel and multiple interfaces data reading and writing to reduce data access latency and thus improve the computing speed. The experimental results show that the LADFastDet model has a mAP of 97.51%, the inference time of the accelerators for a single image is 42.57 ms, and a power consumption of 2.15 W, which achieves a throughput of 24.9 GOPS and an energy efficiency of 11.58 GOPS/W.

关键词： Aluminum surface microdefects detection Lightweight detection network Hardware accelerators Fast detection of defect image signal

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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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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： multi-branch small object detection region clipping region discriminator reinforcement learning agent

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Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems

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DIGITAL signal processing 2025年 156卷

作者： Nguyen, Van Son Duc, Bui Anh Hoang, Tran Manh Tran, Xuan Nam Hiep, Pham Thanh Phuong, Nguyen Thu Hanoi Open Univ Fac Elect & Elect B101 Nguyen Hien Hanoi Vietnam Le Quy Don Tech Univ Adv Wireless Commun Grp 236 Hoang Quoc Viet Hanoi Vietnam Telecommun Univ 101 Mai Xuan Thuong Nha Trang Khanh Hoa Vietnam

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

关键词： Cell free small cell Multi-ARS Channel estimation Pilot assignment

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Sixteenth International Conference on signal processing Systems, ICSPS 2024

Sixteenth International Conference on Signal Processing Syst...

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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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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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EFRNet: Edge feature refinement network for real-time semantic segmentation of driving scenes

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DIGITAL signal processing 2025年 156卷

作者： Hou, Zhiqiang Qu, Minjie Cheng, Minjie Ma, Sugang Wang, Yunchen Yang, Xiaobao Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Key Lab Network Data Anal & Intelligent Proc Shaan Xian 710121 Peoples R China

In the semantic segmentation field, the dual-branch structure is a highly effective segmentation model. However, the frequent downsampling in the semantic branch reduces the accuracy of features expression with increasing network depth, resulting in suboptimal segmentation performance. To address the above issues, this paper proposes a real-time semantic segmentation network based on Edge Feature Refinement (Edge Feature Refinement Network, EFRNet). A dual-branch structure is used in the encoder. To enhance the accuracy of deep features expression in the network, an edge refinement module (ERM) is designed in the dual-branch interaction stage to refine the features of the two branches and improve segmentation accuracy. In the decoder, a Bilateral Channel Attention (BCA) module is designed, which is used to extract detailed information and semantic information of features at different levels of the network, and gradually restore small target features. To capture multi- scale context information, we introduce a Multi-scale Context Aggregation Module (MCAM), which efficiently integrates multi-scale information in a parallel manner. The proposed algorithm has experimented on Cityscapes and CamVid datasets, and reaches 78.8% mIoU and 79.6% mIoU, with speeds of 81FPS and 115FPS, respectively. Experimental results show that the proposed algorithm effectively improves segmentation performance while maintaining a high segmentation speed.

关键词： Dual-branch structure Real-time semantic segmentation Edge refinement Detailed information Multi-scale context information

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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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