Results 41 to 50 of about 82 (67)
Sample‐Core Class Incremental Learning for Radar Target Recognition
IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.This paper proposes a class incremental learning method for radar target recognition. By employing a specialised knowledge distillation strategy and incorporating measures to maintain balance, this method can simultaneously enhance both the stability and plasticity of the model.
Jin Gu, Yuchen Li, Liyu Tian
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Radar HRRP target recognition based on stacked denosing sparse autoencoder
The Journal of Engineering, Volume 2019, Issue 21, Page 7945-7949, November 2019., 2019 An end‐to‐end radar high‐resolution range profile recognition method is proposed based on stacked denosing sparse autoencoder which stacks several denosing sparse autoencoders and uses softmax as the classifier. The training process consists of two steps.
Guangxing Tai +3 more
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IET Radar, Sonar &Navigation, Volume 19, Issue 1, January/December 2025.This paper proposes an improved radar HRRP target recognition method by leveraging a modified Short‐Time Fourier Transform (STFT) module and a Convolutional Neural Network (CNN). The method incorporates multi‐scale analysis and differential processing to enhance feature extraction, demonstrating superior robustness and accuracy across varying signal‐to‐
Xiaohui Wei, Zhulin Zong
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Polarimetric radar target recognition framework based on LSTM
The Journal of Engineering, Volume 2019, Issue 21, Page 8089-8092, November 2019., 2019 Polarimetric information is of great importance for radar target recognition. Conventional polarimetric features are hand‐designed based on scattering mechanism. In this study, a novel polarimetric target recognition framework based on long–short‐term memory (LSTM) network is proposed.
Wei Chen +4 more
wiley +1 more source
HRRPGraphNet: Make HRRPs to be graphs for efficient target recognition
Electronics Letters, Volume 60, Issue 22, November 2024.Conventional techniques for High‐Resolution Range Profiles (HRRP) target recognition frequently encounter difficulties due limited data under non‐cooperated circumstances. We propose a novel use of graph‐theory of HRRPs and convert traditional sequence‐based recognition into a sophisticated graph classification task to achieve efficient target ...
Lingfeng Chen +7 more
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Radar data simulation using deep generative networks
The Journal of Engineering, Volume 2019, Issue 20, Page 6699-6702, October 2019., 2019 Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation.
Yiheng Song, Yanhua Wang, Yang Li
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Adaptive soft threshold transformer for radar high‐resolution range profile target recognition
IET Radar, Sonar &Navigation, Volume 18, Issue 8, Page 1260-1273, August 2024.Aiming to target areas localisation and background noise, a framework termed Adaptive Soft Threshold Transformer (ASTT) is proposed for radar HRRP target recognition, which comprises a PE layer, ASTT blocks, and DWPM layers. Experiments based on a simulated dataset and a measured dataset show that the proposed ASTT has excellent target recognition ...
Siyu Chen, Xiaohong Huang, Weibo Xu
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Remote SensingPolarimetric high-resolution range profile (HRRP), with its rich polarimetric and spatial information, has become increasingly important in radar automatic target recognition (RATR).
Fan Gao +5 more
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Remote SensingRadar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples ...
Yue Dong +6 more
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Leida xuebao, 2019 To address the problem of radar High-Resolution Range Profile (HRRP) target recognition, traditional methods only consider the envelope information of the sample and ignore the temporal correlation between the range cells.
LIU Jiaqi, CHEN Bo, JIE Xi
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