High-resolution vehicle trajectory extraction and denoising from aerial videos
In recent years, unmanned aerial vehicle (UAV) has become an increasingly popular tool for
traffic monitoring and data collection on highways due to its advantage of low cost, high …
traffic monitoring and data collection on highways due to its advantage of low cost, high …
Deep learning-based UAV detection in pulse-Doppler radar
C Wang, J Tian, J Cao, X Wang - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the popularity of unmanned aerial vehicles (UAVs), how to conduct automatic and
effective detection to prevent unauthorized flying has become an important issue. The …
effective detection to prevent unauthorized flying has become an important issue. The …
Magnetotelluric deep learning forward modeling and its application in inversion
F Deng, J Hu, X Wang, S Yu, B Zhang, S Li, X Li - Remote Sensing, 2023 - mdpi.com
Magnetotelluric (MT) inversion and forward modeling are closely linked. The optimization
and iteration processes of the inverse algorithm require frequent calls to forward modeling …
and iteration processes of the inverse algorithm require frequent calls to forward modeling …
Parallel LSTM-CNN network with radar multispectrogram for human activity recognition
Y Qian, C Chen, L Tang, Y Jia, G Cui - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Owing to the ability of automatic feature extraction, deep learning is extensively applied in
human activity recognition (HAR) based on radar. In this article, a recognition method based …
human activity recognition (HAR) based on radar. In this article, a recognition method based …
Multimodel feature reinforcement framework using Moore–Penrose inverse for big data analysis
Fully connected representation learning (FCRL) is one of the widely used network structures
in multimodel image classification frameworks. However, most FCRL-based structures, for …
in multimodel image classification frameworks. However, most FCRL-based structures, for …
Target detection in clutter/interference regions based on deep feature fusion for HFSWR
M Wu, L Zhang, J Niu, QMJ Wu - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
High-frequency surface wave radar (HFSWR) is of great significance for maritime detection,
but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and …
but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and …
DOA estimation for HFSWR target based on PSO-ELM
L Zhang, C Shi, J Niu, Y Ji… - IEEE geoscience and …, 2021 - ieeexplore.ieee.org
High-frequency surface wave radar (HFSWR) plays an important role in vessel target
surveillance. However, HFSWR's inaccuracy of azimuth estimation caused by wide beams …
surveillance. However, HFSWR's inaccuracy of azimuth estimation caused by wide beams …
HKPM: A hierarchical key-area perception model for HFSWR maritime surveillance
High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime
surveillance because of its low-cost maintenance and coverage of wide area. However …
surveillance because of its low-cost maintenance and coverage of wide area. However …
Semisupervised manifold regularization via a subnetwork-based representation learning model
Semisupervised classification with a few labeled training samples is a challenging task in
the area of data mining. Moore–Penrose inverse (MPI)-based manifold regularization (MR) …
the area of data mining. Moore–Penrose inverse (MPI)-based manifold regularization (MR) …
A robust variability index CFAR detector for Weibull background
X Wang, Y Li, N Zhang - IEEE Transactions on Aerospace and …, 2022 - ieeexplore.ieee.org
The Weibull distribution is commonly used for clutter modeling as it can provide a good fit to
the experimental data over a wide range of conditions, and as such there is considerable …
the experimental data over a wide range of conditions, and as such there is considerable …