In the field of synthetic aperture radar (SAR) automatic target recognition (ATR), deep learning-driven methods perform well when numerous training samples are available.
Yan Peng +5 more
doaj +1 more source
Application of advanced technology to space automation [PDF]
Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of ...
Chang, C. Y. +5 more
core +1 more source
Existing synthetic aperture radar (SAR) adversarial attack algorithms primarily focus on the digital image domain, and constructing adversarial examples in real‐world scenarios presents significant and challenging hurdles.
Wei Liu +4 more
doaj +1 more source
CBAM-Enhanced CNN-LSTM with Improved DBSCAN for High-Precision Radar-Based Gesture Recognition. [PDF]
Yi S, Zhao Z, Wu T.
europepmc +1 more source
Radar HRRP Sequence Target Recognition Based on a Lightweight Spatiotemporal Fusion Network. [PDF]
Li X, Su Y, Zhao X, Yin J, Yang J.
europepmc +1 more source
Physics-Guided Variational Causal Intervention Network for Few-Shot Radar Jamming Recognition. [PDF]
Xia D +8 more
europepmc +1 more source
High-Precision Marine Radar Object Detection Using Tiled Training and SAHI Enhanced YOLOv11-OBB. [PDF]
Külcü S.
europepmc +1 more source
Multi-Center Prototype Feature Distribution Reconstruction for Class-Incremental SAR Target Recognition. [PDF]
Zhang K, Wu B, Li P, Kang Z, Zhang L.
europepmc +1 more source
Fusion calib: azimuth angle and multi frame tracking for online extrinsic radar-camera calibration. [PDF]
Cao L +6 more
europepmc +1 more source
A Hybrid Millimeter-Wave Radar-Ultrasonic Fusion System for Robust Human Activity Recognition with Attention-Enhanced Deep Learning. [PDF]
Yao L +7 more
europepmc +1 more source

