4.7 Article

Marine Radar Small Target Classification Based on Block-Whitened TimeFrequency Spectrogram and Pre-Trained CNN

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Computer Science, Information Systems

Multichannel adaptive signal detection: basic theory and literature review

Weijian Liu et al.

Summary: Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine the presence or absence of a target. These adaptive detectors possess constant false alarm rate properties and do not require additional processing. Compared to the filtering-then-CFAR technique, adaptive detection typically exhibits better performance. However, there are few overview articles on this topic, hence this study provides a tutorial overview specifically focusing on Gaussian background and covers various aspects.

SCIENCE CHINA-INFORMATION SCIENCES (2022)

Article Engineering, Electrical & Electronic

Accurate Micro-Doppler Analysis by Doppler and k-Space Decomposition for Millimeter Wave Short-Range Radar

Takeru Ando et al.

Summary: This study presents a highly accurate range and Doppler-velocity extraction scheme for millimeter-wave short-range sensing. By introducing Doppler velocity and k-space decomposition, the proposed method significantly improves range resolution and reduces computational complexity.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

Article Geochemistry & Geophysics

Improved Drone Classification Using Polarimetric Merged-Doppler Images

Byung Kwan Kim et al.

Summary: The study proposed a drone classification method for polarimetric radar based on CNN and image processing methods, which improved accuracy by enhancing weak micro-Doppler features. A novel image structure for three-channel image classification CNN was introduced to utilize received polarimetric signal, along with an image processing method to reduce data size while maintaining high classification accuracy.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

Article Engineering, Electrical & Electronic

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

Xiangyu Gao et al.

Summary: The paper introduces a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts object location and class by processing heatmap sequences, combining lower-dimension NN models to achieve high performance with lower complexity. Experimental results show that the proposed RAMP-CNN model outperforms prior works in various testing scenarios and works robustly during nighttime.

IEEE SENSORS JOURNAL (2021)

Article Computer Science, Information Systems

Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology

Romain Mormont et al.

Summary: In this study, multi-task learning was explored as a method for pre-training models for classification tasks in digital pathology. By assembling and transforming multiple datasets, a pool of 22 classification tasks and nearly 900k images was successfully created. Experimental results showed that our models either significantly outperformed ImageNet pre-trained models or provided comparable performance on different target tasks.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Engineering, Electrical & Electronic

RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization

Yizhou Wang et al.

Summary: This paper presents a deep radar object detection network, RODNet, which can detect objects in RF images in real-time and is cross-supervised by a camera-radar fusion strategy during training. A new dataset CRUW is created for various driving scenarios containing synchronized RGB and RF image sequences. Through extensive experiments, the proposed cross-supervised RODNet achieves robust object detection performance in different driving conditions with 86% average precision and 88% average recall.

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2021)

Article Engineering, Electrical & Electronic

Attribute-Guided Multi-Scale Prototypical Network for Few-Shot SAR Target Classification

Siyuan Wang et al.

Summary: In this article, a novel attribute-guided multi-scale prototypical network (AG-MsPN) combined with subband decomposition is proposed for few-shot SAR target classification, aiming to learn discriminative features from a few labeled data. The AG-MsPN enhances the classification performance under joint supervision of class label information and target attribute information.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

Article Acoustics

CSS-LM: A Contrastive Framework for Semi-Supervised Fine-Tuning of Pre-Trained Language Models

Yusheng Su et al.

Summary: A novel framework (CSS-LM) is introduced to improve the fine-tuning phase of PLMs through contrastive semi-supervised learning, helping PLMs capture crucial semantic features in downstream tasks with few-shot settings.

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING (2021)

Article Engineering, Electrical & Electronic

CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available

Hui Bi et al.

Summary: The article introduces a novel framework for target detection and classification based on sparse SAR images, utilizing complex approximate message passing (CAMP) to generate the sparse SAR image dataset and convolutional neural network technology for target detection and classification. Under standard operating conditions, the framework achieves high mAP values using the D-Nsp sparse SAR image dataset.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

Article Computer Science, Information Systems

A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data

Zachary Baird et al.

IEEE ACCESS (2020)

Article Automation & Control Systems

Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

Li Binquan et al.

JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS (2019)

Article Computer Science, Interdisciplinary Applications

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

Article Geochemistry & Geophysics

An Efficient Method for Detecting Slow-Moving Weak Targets in Sea Clutter Based on Time-Frequency Iteration Decomposition

Lei Zuo et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2013)

Article Engineering, Electrical & Electronic

A Template Matching Procedure for Automatic Target Recognition in Synthetic Aperture Sonar Imagery

Vincent Myers et al.

IEEE SIGNAL PROCESSING LETTERS (2010)

Article Geochemistry & Geophysics

Analysis of sea spikes in radar sea clutter data

HW Melief et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2006)

Article Engineering, Electrical & Electronic

Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection

E Conte et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2002)