Bin Xu
Xidian University
5 Papers
3 Citations
Bin Xu is an academic researcher from Xidian University. The author has contributed to research in topics: Computer science & Feature (machine learning). The author has an hindex of 3, co-authored 4 publications.
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Papers
Convolutional neural networks for radar HRRP target recognition and rejection
TL;DR: This paper takes advantage of the attractive properties of convolutional neural networks (CNNs) to address HRRP RATR and rejection problem and devise a two-dimensional CNN model for the spectrogram feature.
Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition
TL;DR: A Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension.
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Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition
TL;DR: A conditional generative model for radar high resolution range profile (HRRP) target recognition to learn the discriminative representations and sufficiently encode the observed feature variability by taking the multi-layer perception (MLP) as the sufficient statistics of posterior approximation distribution, thus offering the potential to improve the overall recognition performance.
82
Gaussian Mixture Model-Tensor Recurrent Neural Network for HRRP Target Recognition
Bin Xu,Bo Chen,Jiaqi Liu,Chuan Du +3 more
- 01 Sep 2019
TL;DR: A Gaussian Mixture Model Tensor Recurrent Neural Network (GMM-TRNN) is proposed for Radar Automatic Target Recognition (RATR) based on the hypothesis that the temporal correlation between different range cells in the HRRP sample being different, which achieves competitive recognition performance compared with traditional methods.
5
Construction Method of the Distribution Transform Load Feature Database Based on Deep Convolutional Autoencoder
TL;DR: A method to build a prosumer load characteristic database based on a deep convolutional autoencoder network and the comparison between the clustering index and the traditional k-means clustering algorithm and the improved k-Means direct clustering algorithms proves that the method can effectively improve the accuracy of clustering results.