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检索条件"任意字段=Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII"

共 1034 条记录，以下是1-10 订阅

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Multi-sensor fusion based in-process monitoring technology for vertical milling cutter damage and breakage detection 4

Multi-sensor fusion based in-process monitoring technology f...

引用

4th International conference on Advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Dong, Chunping Zhang, Xi School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

With the development of signal processing technology, statistical analysis, time domain analysis, frequency domain analysis and time-frequency domain analysis based on sensor signals enrich the scope of feature extraction. The development of modern manufacturing technology makes the monitoring mode transition from the traditional single sensor to the multi-sensor fusion monitoring mode. Aiming at the failure of tool breakage and tool breakage, this paper proposes an on-line monitoring method of milling tool breakage and tool breakage based on the fusion of spindle load and acoustic emission signal. The eigenvalues of acoustic emission signal and spindle load signal are fused to establish the feature vector, and the tool status decision model based on support vector machine is established. Compared with the single signal monitoring method, the tool status recognition accuracy of the signal fusion method proposed in this paper is significantly improved, which overcomes the problems of low accuracy and poor fault tolerance of tool status recognition of single signal. The kernel parameters and penalty factors of SVM are optimized by grid search, genetic algorithm and particle swarm optimization algorithm respectively. The research shows that the support vector machine decision model optimized by genetic algorithm has the best effect on the classification of milling cutter breakage and cutter breakage, and the fusion of the two sensor signals also improves the accuracy of the detection of breakage and cutter breakage. The feasibility of this method is verified by two sets of actual cutting experiments. © 2025 SPIE.

关键词： Milling cutters

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fusion feature multi-objective classification algorithm based on adaptive scale feature residual network 16

Fusion feature multi-objective classification algorithm base...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Hua, Chenyang Xie, Renhong Li, Peng Fang, Gan Zhao, Xianya Zhou, Maoyuan School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781510689251

With the development of science and technology, radar has been widely used in both civil and military fields, and radar target classification and recognition is an important research direction in radar signal processing field. The traditional classification and recognition technology does not utilize all the information of echo, and the recognition rate will decrease obviously when the environment is too complicated and the SNR is low. To solve the above problems, this paper proposes three recognition algorithms. It is a fusion feature multi-objective classification algorithm based on multi-scale feature residual network (MKRN), a fusion feature multi-objective classification algorithm based on adaptive scale feature residual network (AKRN) and a fusion feature multi-objective classification algorithm based on adaptive scale feature multi-level branch residual network (AKSRN). These three algorithms introduce residual structure to avoid network degradation caused by the deepening of network depth, and use convolution kernel of different sizes to enhance the ability of the network to extract multi-scale feature information, which improves the accuracy of network classification and recognition. © 2025 SPIE.

关键词： Radar signal processing

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Human Action recognition Method based on Spatiotemporal Decoupling Multi-Scale Feature fusion Network 16

Human Action Recognition Method based on Spatiotemporal Deco...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Xie, Jigang Wan, Kai Tian, Cheng Li, Junhuai Hu, Shuai Wang, Huaijun China Unicom Western Innovation Research Institute Co. Ltd. China Xi'an University of Technology China

ISBN: (纸本)9781510689251

Aiming at the problems of incomplete expression ability of a single neural network, ignoring the spatiotemporal characteristics of time series data and slow reasoning speed and low accuracy of the traditional network model in wearable behavior recognition tasks, this paper proposes a human behavior recognition method based on spatiotemporal decoupling multi-scale feature fusion network. Firstly, the Multi-Dimensione Convolutional Neural Network (MDCNN) is used to extract the different scale features of multi-channel sensor data, and the shallow and deep features of the signal are extracted at the same time. The complementary information between various levels of features is fully utilized to extract the features that represent the action information. Then, through the Decoupling Temporal-Spatially Transformer (DTST) based on the Transformer, the network is decoupled into time decoupling and space decoupling structures, to accurately extract the spatiotemporal features in the inertial signal. Finally, the Softmax classifier is used for action classification and recognition. The experimental results show that the proposed method can effectively utilize the consistency and complementarity between multi-sensor data, fuse multi-scale spatiotemporal features, and improve the reasoning performance and recognition accuracy in human behavior recognition. The average accuracy rate on the UCI-MHEALTH dataset is 96.83 %, and the average F1 value is 96.81 %. © 2025 SPIE.

关键词： Convolutional neural networks

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Dual-band Image Sequence Registration based on Description of Velocity and Curvature Distribution 16

Dual-band Image Sequence Registration based on Description o...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Bingqing Tang, Shanjun Li, Chuchen Yang, Beier Yang, Qingyu Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infrared and visible light images. The quality of the fused image is closely related to the image registration accuracy. In order to solve the problem that the feature description of infrared and visible dual-band images is not identical due to large difference in grayscale texture information, we propose a registration algorithm based on description of velocity and curvature distribution. In particular, we first extract the infrared and visible light target foreground, and use the created velocity distribution description to complete the FAST corner point matching on the foreground contour. All the matched point pairs are used to achieve coarse contour registration. Then, we use the created curvature distribution description to complete the CSS corner point matching of the target foreground. Finally, a real-time updated matching reservoir is built to calculate the fine registration matrix. The experimental results of multiple sets of actual infrared/visible light image sequences show that the proposed algorithm can achieve better feature description identity and better registration accuracy than the other algorithms. © 2025 SPIE.

关键词： Image fusion

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signal processing, sensor/information fusion, and target recognition xxviii

Signal Processing, Sensor/Information Fusion, and Target Rec...

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signal processing, sensor/information fusion, and target recognition xxviii 2019

ISBN: (纸本)9781510627017

The proceedings contain 57 papers. The topics discussed include: problems with information fusion under extreme multiple-counting conditions;a fuzzy inference system for ship-ship collision alert generation;maritime situational awareness with OCULUS Sea C2I and forensics tools for a common information sharing environment (CISE);a GLMB filter for unified multitarget multisensor management;sequential and parallel fusion of detection and classification systems;sequence theory for classification in multi-label ATR classification tasks;extremely deep Bayesian learning with Gromov's method;information fusion to estimate resilience of dense urban neighborhoods;and an agent-administrator-based security mechanism for distributed sensors and drones for smart grid monitoring.

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signal processing, sensor/information fusion, and target recognition xxviii

Signal Processing, Sensor/Information Fusion, and Target Rec...

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signal processing, sensor/information fusion, and target recognition xxviii 2019

ISBN: (纸本)9781510627017

The proceedings contain 30 papers. The topics discussed include: probabilistic reasoning for real-time UAV decision and control;the effect of atmospheric optical turbulence on laser communication systems — part 2, practice;the effect of atmospheric optical turbulence on laser communication systems — part 1, theory;proton and gamma radiation testing of 10 GHz bandwidth, uncooled, linear InGaAs optical receivers;toolchain based hybrid implementation for GPS satellite communications with optical crosslinks;an effective satellite transponder linearization method using a physics-based predistorter;SDR based secure communication system with novel noise modulated transmission;face recognition in low-resolution surveillance video streams;and decentralized smart surveillance through microservices platform.

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Text-guided Multimodal fusion for the Multimodal Emotion and Intent Joint Understanding

Text-guided Multimodal Fusion for the Multimodal Emotion and...

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

作者： Yu Zhang Bin Chen Hongfei Ye Zijian Gao Tianjiao Wan Long Lan Kele Xu University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Emotion and Intent Joint Understanding in Multi-modal Conversation is a challenging task in the field of affective computing, aiming to decode the semantic information manifested in the multimodal conversational while simultaneously inferring the emotions and intents of the utterance. To address this challenge, we propose the Text-guided Multimodal Emotion-Intent Joint recognition method. By leveraging the text modality to guide the fusion process, it effectively reduces the noise introduced by other modalities. To strengthen the text modality’s guiding role, we use large language models (LLMs) for multi-turn targeted data augmentation and oversampling strategies to address data imbalance. Our approach achieved first place in Track 1 (English) of the ICASSP 2025 MEIJU Challenge, demonstrating its effectiveness in practical applications.

关键词： Emotion recognition target tracking Large language models Noise Semantics Machine learning Interference Data augmentation Robustness Speech processing

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target Matching Method for Radar Emitter Based on Sequence Similarity

Target Matching Method for Radar Emitter Based on Sequence S...

引用

IEEE information Technology and Mechatronics Engineering conference (ITOEC)

作者： Xiaobo Zheng Southwest China Research Institute of Electronic Equipment Chengdu China National Key Laboratory Of Electromagnetic Space Security Chengdu China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

For cooperative combat scenarios involving electronic countermeasure platforms, a method is proposed to perform target matching and association of radar emitters at the scale of hundreds of milliseconds or seconds. Due to the different fields of view of the platforms, the received radar emitter signal sequences are incomplete. The method uses Levenshtein distance to calculate the distance between parameter sequences within a tolerance range, thereby computing the similarity of the parameter sequences. It then uses empirical factors to weight and obtain the target similarity, completing the target matching. The method is simple to implement and convenient for equipment application, and the matching results can be directly used for the fusion of identification results, such as model recognition, in subsequent collaborative processing.

关键词： Deep learning Mechatronics Computational modeling Collaboration Radar Hardware Radar countermeasures information technology Electronic countermeasures

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

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

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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Evaluating Killer Drone Defense: NATO SPS Project "Anti-Drones" Field Trials 16th

Evaluating Killer Drone Defense: NATO SPS Project "Anti-Dron...

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16th International conference on ICT Innovations, ICT Innovations 2024

作者： Lupidi, Alberto Mancuso, Francesco Meucci, Giulio Jajaga, Edmond Rushiti, Veton Cantelli-Forti, Alessandro Radar and Surveillance Systems National Laboratory CNIT Pisa56124 Italy Department of Information Engineering University of Pisa Pisa56122 Italy Mother Teresa University Skopje1000 Macedonia

ISBN: (纸本)9783031861611

The growing number of Unmanned Aerial Vehicles (UAVs) in both military and civilian sectors necessitates advanced tracking and detection systems. This paper presents the development and field trial results of a high-resolution radar and optical system for UAV detection conducted within the NATO SPS project "Anti-Drones". By using a multichannel linear frequency modulated continuous wave (FMCW) radar architecture, the system demonstrates robust capabilities in identifying and tracking various UAV types, including "mini" and "micro" drones. Field trials evaluated detection accuracy, tracking reliability, and payload recognition. Advanced signal processing and machine learning (ML) algorithms enhanced real-time recognition and reduced false alarms. Integrating radar data with optical camera tracking further improved performance in multi-target scenarios. This study highlights the system’s potential for military and civilian applications in addressing UAV threats. Future research will focus on refining data fusion algorithms and extending operational range based on trial insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones

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