Results 31 to 40 of about 97 (71)
Remote Sensing, 2021 Radar automatic target recognition is a critical research topic in radar signal processing. Radar high-resolution range profiles (HRRPs) describe the radar characteristics of a target, that is, the characteristics of the target that is reflected by the ...
Chih-Lung Lin +4 more
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Open set HRRP recognition based on convolutional neural network
The Journal of Engineering, Volume 2019, Issue 21, Page 7701-7704, November 2019., 2019 Most existing algorithms in high‐resolution range profile recognition focus on the closed set cases, where the test sample is from a known class. However, a sample could be drawn from unknown classes in realistic scenario, which is named as open set recognition.
Wei Chen, Yanhua Wang, Jia Song, Yang Li
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Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism
IEEE Access, 2020 In this paper, we integrate the advantages of convolutional neural network (CNN) and bidirectional recurrent neural network (BiRNN) with attention mechanism, and propose a CNN-BiRNN based method to recognize the individual high resolution range profile ...
Jinwei Wan +5 more
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Deep forest for radar HRRP recognition
The Journal of Engineering, Volume 2019, Issue 21, Page 8018-8021, November 2019., 2019 High‐resolution range profile (HRRP) has received intensive attention in the radar automatic target recognition filed. Here, deep forest is applied to the recognition of HRRP. The deep forest is a deep learning method, which is a cascade of ensemble learners. In each layer, there are various ensemble learners. The input of each layer is the combination
Yanhua Wang +5 more
wiley +1 more source
Radar HRRP recognition based on CNN
The Journal of Engineering, Volume 2019, Issue 21, Page 7766-7769, November 2019., 2019 In this study, ground target recognition based on one‐dimensional convolutional neural network (CNN) is studied by exploiting the targets’ high‐resolution range profiles (HRRPs). Contrary to conventional methods which need feature extraction artificially, CNN can automatically discover features for classification.
Jia Song +4 more
wiley +1 more source
Polarised HRRP scattering centre estimation via atomic norm minimisation
The Journal of Engineering, Volume 2019, Issue 21, Page 7847-7850, November 2019., 2019 The combination of polarisation and high‐resolution technology is a promising research direction for radar automatic target recognition. Fusing the polarisation information into the scattering centre model is able to refine the scattering structural information. This study proposes a fully‐polarised radar high range resolution profile (HRRP) scattering
Haibo Liu +4 more
wiley +1 more source
Noise-Robust Radar High-Resolution Range Profile Target Recognition Based on Residual Scattering Attention Network [PDF]
In recent years, radar automatic target recognition (RATR) utilizing high-resolution range profiles (HRRPs) has received significant attention. Approaches based on deep learning have demonstrated remarkable efficacy in HRRP recognition tasks.
Muhai Zheng +5 more
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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
wiley +1 more source
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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