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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

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Experimental Investigation for GPR Detection of Buried Objects with Varied Sizes and Orientation

Experimental Investigation for GPR Detection of Buried Objec...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Sujeet Roy Himanshu Govil Sandip Mukherjee Prabhat Diwan Department of Applied Geology National Institute of Technology Raipur India Independent Researcher Indian Institute of Remote Sensing Dehradun India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Ground Penetration Radar (GPR) is a remote sensing technology with immense potential. Its performance and quality of results strongly depends on primarily the composition of the soil and further on other aspects of the subsurface targets like the size, material, shape, depth, and orientation. The central operating frequency of the GPR and the type of antenna on the GPR are selected based on the factors above, to be able to detect your intended target satisfactorily. Limited exploratory research work has been undertaken in this field in India towards detection of small shallow buried objects. The present study attempts to experimentally investigate the combined effect of predetermined size and orientation of small shallow buried objects in conjunction with a suitably selected central frequency of the GPR. The experiment was conducted in Jaisalmer, where the arid dry sandy wastelands are conducive to GPR performance. signal processing methods have been applied to the standard GPR results to derive better output. Apart from the standard effect of material of the targets and the depth buried to, the study mainly provided data indicating a positive correlation between the central operating frequency of the GPR and the quality of detection of the shallow subsurface targets of size corresponding to the wavelength $\lambda$. The positive correlation of polarization of the GPR antenna to the alignment of the buried object was also noted. Further investigations covering more factors affecting GPR performance is advocated by the present study.

关键词： Surveys Correlation Soil Planning data mining Test equipment Buried object detection Standards Remote sensing Antennas

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DF-CFAR: deep feature constant false alarm ratio detector based on feature game for sea-surface small target with robustness to sea states

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Neural Computing and Applications 2025年 1-20页

作者： Xiang, Houhong Zhu, Liangliang Chen, Yufeng Wang, Fengyu Zeng, Xiaolu School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Early Warning Technology Wuhan Early Warning Academy Wuhan430019 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

The classical constant false alarm rate (CFAR) detector is optimal for target detection in Gaussian white noise but struggles with unknown, time-varying sea states. data-driven target detection methods are highly sensitive to clutter distribution, leading to poor detection performance and high false alarm rates (Pfa) under unknown sea states. This paper proposes a deep feature constant false alarm ratio (DF-CFAR) detector based on a feature game model. The input to the feature extraction network is fragmented fast time-dimensional data processed by coherent integration, incorporating a feature game mechanism. This mechanism effectively mines target echo features that are uncorrelated with the background sea clutter. By transforming the target detection problem under varying sea state conditions into a regression prediction problem, we achieve constant false alarm detection through a statistical probability threshold. Simulation and real data experiments demonstrate that, compared to commonly used algorithms, the proposed method not only achieves higher detection probability and lower false alarm rates but also exhibits excellent constant false alarm characteristics and stronger robustness to unseen sea states. Results from publicly available X-band radar data processing show that the proposed algorithm can eliminate the influence of different sea clutters and improve the detection performance of sea-surface small targets by an equivalent signal-to-clutter ratio (SCR) of about 3.1 dB. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Gaussian distribution

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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Group Information Geometry Approach for Ultra-Massive MIMO signal Detection

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IEEE Transactions on signal processing 2025年

作者： Yang, Jiyuan Fan, Mingrui Chen, Yan Gao, Xiqi Xia, Xiang-Gen Slock, Dirk Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States EURECOM Department of Communication Systems Biot06410 France

We propose a group information geometry approach (GIGA) for ultra-massive multiple-input multiple-output (MIMO) signal detection. The signal detection task is framed as computing the approximate marginals of the a posteriori distribution of the transmitted data symbols of all users. With the approximate marginals, we perform the maximization of the a posteriori marginals (MPM) detection to recover the symbol of each user. Based on the information geometry theory and the grouping of the components of the received signal, three types of manifolds are constructed and the approximate a posteriori marginals are obtained through m-projections. The Berry-Esseen theorem is introduced to offer an approximate calculation of the m-projection, while its direct calculation is exponentially complex. In most cases, increasing the number of groups tends to reduce the computational complexity of GIGA. However, when the number of groups exceeds a certain threshold, the complexity of GIGA starts to increase. Simulation results confirm that the proposed GIGA achieves better bit error rate (BER) performance within a small number of iterations, which demonstrates that it can serve as an efficient detection method in ultra-massive MIMO systems. © 1991-2012 IEEE.

关键词： Computational geometry

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基于导航雷达回波视频数据的占据栅格地图构建方法

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中国舰船研究 2025年第1期20卷 96-106页

作者：江坤颐孙世平蒋丙栋蔡庆江苏自动化研究所江苏连云港222061 中船(北京)智能装备科技有限公司北京102600

[目的]为解决无人艇的船载导航雷达对养殖区、浮筒、小型漂浮物等海洋漂浮障碍物感知效果不佳的问题,提出一种基于导航雷达回波视频数据构建与更新的占据栅格地图的环境感知方法。[方法]首先,采用多级集合的形式描述雷达点迹与回波点间... 详细信息

[目的]为解决无人艇的船载导航雷达对养殖区、浮筒、小型漂浮物等海洋漂浮障碍物感知效果不佳的问题,提出一种基于导航雷达回波视频数据构建与更新的占据栅格地图的环境感知方法。[方法]首先,采用多级集合的形式描述雷达点迹与回波点间的包含关系,为栅格地图构建奠定基础,期间,基于群相邻关系对近邻点迹进行凝聚,抑制目标分裂导致的航迹偏差;然后,利用所提的基于自然对数函数的占据栅格地图概率更新算法,通过合理利用历史数据区分海杂波与微小海洋漂浮障碍物;最后,建立基于点迹属性的栅格地图概率扩散模型,以较好地保证典型动态目标占据栅格更新的实时性。[结果]实船试验结果表明,所提方法可准确获取养殖区、浮筒等成片海洋漂浮障碍物的轮廓信息,抑制目标分裂现象;与经典方法相比,所提方法对干舷0.5m的小型漂浮物首次发现距离提升了78.34m,定位精度提升了1.42m。[结论]所提方法能够实现对多种海洋漂浮障碍物、海面运动目标的准确感知,确保无人艇航行安全。

关键词：无人船导航雷达雷达信号处理目标识别雷达回波信号态势感知漂浮障碍物占据栅格地图

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AN EFFICIENT ALGORITHM FOR TRACKING THE ANGLES OF ARRIVAL OF MOVING targets

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IEEE TRANSACTIONS ON signal processing 1991年第1期39卷 242-246页

作者： SASTRY, CR KAMEN, EW SIMAAN, M Signal Processing and Interpretation Laboratory Department of Electrical Engineering University of Pittsburgh Pittsburgh PA USA

In this work a new technique for tracking the angles of arrival of moving targets is presented. The targets are modeled as signal sources that continuously emit narrow-band signals which impinge on an array of sensors. Estimates of target angles are obtained by minimizing the norm of an error matrix function involving the covariance of the sensor outputs. The algorithm yields estimates that are automatically correctly associated with previous estimates. Consequently, the data association problem does not arise, and this results in a much more efficient scheme in comparison to existing methods involving a search over N! possible measurement/target associations (where N is the number of targets). Performance of the algorithm is illustrated by a simulation example.

关键词： Target tracking Sensor arrays signal processing algorithms Covariance matrix Filtering Narrowband Cleaning Smoothing methods Filters Image enhancement

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MID-COURSE MULTITARGET TRACKING USING CONTINUOUS REPRESENTATION

MID-COURSE MULTITARGET TRACKING USING CONTINUOUS REPRESENTAT...

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CONF ON signal AND data processing OF small targets

作者： ZAK, M TOOMARIAN, N Jet Propulsion Lab. (United States)

ISBN: (纸本)0819405906

The thrust of this paper is to present a new approach to multi-target tracking for the mid-course stage of the Strategic Defense Initiative (SDI). This approach is based upon a continuum representation of a cluster of flying objects. We assume that the velocities of the flying objects can be embedded into a smooth velocity field. This assumption is based upon the impossibility of encounters in a high density cluster between the flying objects. Therefore, the problem is reduced to an identification of a moving continuum based upon consecutive time frame observations. In contradistinction to the previous approaches, here each target is considered as a center of a small continuous neighborhood subjected to a local-affine transformation, and therefore, the target trajectories do not mix. Obviously, their mixture in plane of sensor view is apparent. The approach is illustrated by an example.

关键词： Sensors data processing signal processing Clouds Defense and security Aerospace engineering Defense technologies Numerical simulations Microelectronics Particles

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Recognition of moving ground targets by measuring and processing seismic signal

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MEASUREMENT 2005年第2期37卷 189-199页

作者： Lan, JH Nahavandi, S Lan, T Yin, YX Univ Sci & Technol Beijing Informat Engn Sch Dept Measurement & Control Technol Beijing 100083 Peoples R China Deakin Univ Sch Engn & Technol Geelong Vic 3217 Australia Oregon Hlth Sci Univ OGI Sch Sci & Engn Dept Biomed Engn Beaverton OR 97006 USA

Because vehicles moving over ground generate a succession of impacts, the soil disturbances propagate away from the source as seismic waves. Thus, in the battlefield environment, we can detect moving ground vehicles by means of measuring seismic signals using a seismic velocity transducer, and automatically classify and recognize them by advance signal processing method. Because seismic sensor is easy to be developed by emerging micro-electro-mechanical system (MEMS) technology, seismic detection that will be low cost, low power, small volume and light weight is a promising method for moving ground targets. Such a detection method can be used in many different fields, such as battlefield surveillance. traffic monitoring, law enforcement and so on. The paper researches seismic signals of typical vehicle targets in order to extract features of seismic signal and to recognize targets. As a data fusion method, the technique of artificial neural networks (ANN) is applied to recognize seismic signals for vehicle targets. An improved BP algorithm and ANN data fusion architecture have been presented to improve learning speed and avoid local minimum points in error curve. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. It can be proven that moving ground vehicles can be detected by measuring seismic signal, feature extraction of target seismic signal is correct and ANN data fusion is effective to solve the recognition and classification problem for moving ground targets. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： target recognition seismic signal data fusion

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signal processing for large bandwidth and long duration waveform SAR

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MULTIDIMENSIONAL SYSTEMS AND signal processing 2003年第1-3期14卷 119-137页

作者： Lin, ZP Zeng, YZ Bi, G Yeo, J Nanyang Technol Univ Sch EEE Singapore 639798 Singapore Natl Univ Def Technol Changsha Hunan Peoples R China DSO Natl Labs Singapore Singapore

We first show that for some kinds of signals a "bandwidth and time duration reduction technique" can be used to simulate waveform distortions caused by moving targets, that is, it is correct to measure the waveform distortions at very large TB with relatively small v by reducing TB and increasing v while keeping TBv unchanged, where T is the duration of the transmitted signal, B is the bandwidth and v is the relative speed of targets. We then study the waveform distortions in SAR signals caused by moving antenna. Based on the bandwidth and time duration reduction technique, a lot of time and memory are saved in simulations. We then confirm by simulations that waveform distortions do pose problems when processing very large bandwidth and long duration SAR data using conventional SAR processing methods. Finally we propose the concepts of wideband and narrowband processing of SAR data. Models are set up for wideband and narrowband SAR data processing, and new methods are presented for reconstructing targets using the proposed models. Simulations show that the methods can improve the quality of the simulated SAR images.

关键词： large bandwidth long duration waveform SAR data processing SAR model SAR image

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