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共 447 条记录，以下是1-10 订阅

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Machine learning classifier for marine radar small targets based on normalization features 16

Machine learning classifier for marine radar small targets b...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Zi-Jian Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

targets on the sea surface are of great concern, especially the classification of small targets, which is crucial for marine safety. therefore, we propose a machine learning classifier for marine radar small targets based on normalization features, which can effectively classify targets with small radar cross section (RCS) and signal-to-clutter ratio (SCR) on the sea surface. We standardize the radar parameters because different radars have different system parameters, resulting in limited generalization ability of the trained classifier. We extract 9 features from multiple dimensions of the signal such as time domain, frequency domain and time-frequency domain and then classify targets using decision tree (DT) classifier, K-nearest neighbors (KNN) classifier, bootstrap aggregation (Bagging) classifier and adaptive boosting (AdaBoost) classifier. Experimental validation is performed on publicly available IPIX, self-measured radar data and data provided by the South African Council for Scientific and Industrial Research (CSIR), which contains four types of targets: floating ball, floating boats, low-altitude unmanned aerial vehicles (UAVs) and moving speedboats, and the results of the experiments show that the proposed classifier achieves a correct classification accuracy of 0.8537. © 2025 SPIE.

关键词： Marine radar

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Research on Image Quality Assessment Methods for small Target data Augmentation under the YOLO Framework 24

Research on Image Quality Assessment Methods for Small Targe...

引用

13th International conference on Computing and Pattern Recognition, ICCPR 2024

作者： Liu, Zhibin Liu, Pengyu Cui, Bifeng Li, Gang Deng, Jun Li, Qian Department of Information Science Beijing University of Technology Beijing China

ISBN: (纸本)9798400717482

When using image scaling algorithms for data augmentation on small targets within the YOLOv framework, it was observed that the ranking of different image scaling algorithms based on their data enhancement effects significantly diverges from the rankings provided by mainstream image quality assessment methods based on the human visual system. this indicates that the machine vision system of YOLOv5 has different requirements for image quality compared to the human visual system. By analyzing various image quality assessment methods, this study identified an image quality assessment method, BLIINDS-II, that is highly correlated with the data enhancement effects of YOLOv5. It was found that YOLOv5's assessment of image quality is derived from the non-DC coefficients of the image following a Discrete Cosine Transform (DCT), focusing on the high-frequency components of the image. © 2024 Copyright held by the owner/author(s).

关键词： Discrete cosine transforms

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Sixteenth International conference on signal processing Systems, ICSPS 2024

Sixteenth International Conference on Signal Processing Syst...

引用

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

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

引用

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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Tensor Decomposition-Based Feature Transfer Learning for BCI Applications

Tensor Decomposition-Based Feature Transfer Learning for BCI...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Zahra Sohrabi Bonab Mohammad Bagher Shamsollahi Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Event-Related Potential (ERP) detection and enhancement remain significant challenges in EEG signal processing due to the complex and multidimensional nature of ERP signals. this paper addresses these challenges by proposing a novel framework that combines low-rank tensor representation with heuristic feature transfer learning for efficient ERP processing. By leveraging tensor decomposition, we capture intricate multidimensional patterns in ERP data, facilitating the extraction of informative features. these features greatly reduce processing time, making the method particularly suitable for online Brain-Computer Interface (BCI) applications. We also present a feature transfer learning mechanism utilizing tensor decomposition, which facilitates the efficient adaptation of features trained on multiple subjects to new subjects, requiring only a small amount of calibration data. this approach leverages shared classification structures across subjects, allowing for rapid deployment and minimizing the need for extensive retraining. Our results demonstrate that the proposed method achieves reasonable ERP detection accuracy while drastically reducing computational overhead, presenting a robust solution for real-time BCI systems.

关键词： Training Tensors Accuracy Transfer learning Training data signal processing Feature extraction Real-time systems Neurorehabilitation data mining

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Fast small Object Detection Algorithm Based on Feature Enhancement and Reconstruction 13

Fast Small Object Detection Algorithm Based on Feature Enhan...

引用

13th International conference on Wireless Communications and signal processing, WCSP 2021

作者： Huo, Zhiyong Yan, Tianwen Cao, Weiye Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

small object detection is one of the research hotspots and challenges in the field of machine vision. Because small targets have fewer pixels in the image, the number of features captured by the first few layers of the target detection network is very small, and the high-level semantic information such as feature classification disappears quickly, making the accuracy of target detection relatively low. the paper uses sub-pixel convolution to reconstruct feature information, uses SE (Squeeze-and-Excitation) attention mechanism to enhance features, and uses U-Net network to capture feature context information and has the advantages of faster detection speed. the algorithm realizes the fast and accurate identification of small targets. Numerical experimental results on the COCO data set and the SUN data set show that the algorithm improved performance by more than 12% in the case of the bounding box AP compared with mainstream algorithms such as YOLOv4. © 2021 IEEE.

关键词： Convolution

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Tracking with asynchronous multiple sensors

Tracking with asynchronous multiple sensors

引用

13th conference on signal and data processing of small targets

作者： Gray, JE McCabe, DH USN Ctr Surface Warfare Dahlgren Div Syst Res & Technol Dept Dahlgren VA 22448 USA

ISBN: (纸本)0819441872

Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the, same operation at the identical time), take measurements at the same time and have no communication delays between sensor platforms and the central processing center. Such assumptions are not valid in practice. this paper removes these assumptions when dealing with multisensor target tracking. In particular, it assumes that the sensors used can have different data rates and communication delays between local and central platforms. A new tracking algorithm using asynchronous sensors is proposed and derived in this paper.

关键词： Filtering (signal processing) Lithium Sensors Silicon Zinc Electronic filtering Lutetium data communications data processing signal processing

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Tracking airborne targets occasionally hidden in the blind Doppler

Tracking airborne targets occasionally hidden in the blind D...

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13th conference on signal and data processing of small targets

作者： Gordon, NJ Ristic, BO QinetiQ Ltd Malvern WR14 3PS Worcs England

ISBN: (纸本)0819441872

this paper describes an application of sequential Monte Carlo estimation (particle filtering) to the problem of tracking targets occasionally hidden in the blind Doppler zones of a radar. A particle filter which incorporates the prior knowledge of the blind Doppler zone limits has been designed. the simulation results suggest significant improvement in track continuity over the standard Extended Kalman filter. As an operationally viable solution a hybrid tracker is envisaged which can switch between the EKF (with possibly built-in data association logic) and the particle filter, depending on the tracking conditions.

关键词： target tracking blind Doppler Bayesian methods bootstrap filter particle filter

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Cover intervals for a maneuvering target using bearing and weak range measurements

Cover intervals for a maneuvering target using bearing and w...

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13th conference on signal and data processing of small targets

作者： Sworder, DD Boyd, JE Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819441872

Without range measurements, a sensor platform must execute a nontrivial motion if good target location estimates are to be generated with conventional tracking algorithms. this paper shows that even a stationary image-based tracker can provide good location estimates when the target maneuvers. A tight cover region is generated with the proposed algorithm, and is compared with a more general bound.

关键词： nonlinear filters target tracking data fusion

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Target selection using a utility theoretic approach

Target selection using a utility theoretic approach

引用

13th conference on signal and data processing of small targets

作者： Özener, MS Yardimci, Y Orta Dogu Teknik Univ Enformat Ensitusu TR-06351 Ankara Turkey

ISBN: (纸本)0819441872

Modern target tracking systems perform different tasks among which are target detection, selection and tracking. in this study, we describe a target selection technique based on Utility theory. the utility of each target is assumed to be a linear combination of some basis functions that act on certain features of the target such as size, intensity, speed and direction. the unknown parameters of the basis functions and their weights are selected using a descent type optimization technique. Simulation results are presented.

关键词： Target detection Cameras Automatic tracking data processing Infrared imaging Infrared radiation MATLAB Neural networks Sensors signal processing

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