Two joint time-frequency transforms for velocity separation of moving target radar data

2007 ◽  
Author(s):  
Matthew Ferrara ◽  
Gregory Arnold ◽  
Margaret Cheney
Keyword(s):  
Radar Data ◽  
Moving Target ◽  
Time Frequency

Adaptive joint time-frequency analysis for focusing ISAR images from simulated and experimental radar data

2004 ◽  
Author(s):  
Thayananthan Thayaparan ◽  
George Lampropoulos ◽  
Silvester K. Wong ◽  
Edwin Riseborough
Keyword(s):  
Frequency Analysis ◽  
Radar Data ◽  
Time Frequency Analysis ◽  
Time Frequency

scholarly journals GMT-WGAN: An Adversarial Sample Expansion Method for Ground Moving Targets Classification

2021 ◽  
Vol 14 (1) ◽  
pp. 123
Author(s):  
Xin Yao ◽  
Xiaoran Shi ◽  
Yaxin Li ◽  
Li Wang ◽  
Han Wang ◽  
...  
Keyword(s):  
Feature Learning ◽  
Expansion Method ◽  
Classification Performance ◽  
Moving Target ◽  
Moving Targets ◽  
Target Classification ◽  
Deep Convolutional Neural Networks ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network

In the field of target classification, detecting a ground moving target that is easily covered in clutter has been a challenge. In addition, traditional feature extraction techniques and classification methods usually rely on strong subjective factors and prior knowledge, which affect their generalization capacity. Most existing deep-learning-based methods suffer from insufficient feature learning due to the lack of data samples, which makes it difficult for the training process to converge to a steady-state. To overcome these limitations, this paper proposes a Wasserstein generative adversarial network (WGAN) sample enhancement method for ground moving target classification (GMT-WGAN). First, the micro-Doppler characteristics of ground moving targets are analyzed. Next, a WGAN is constructed to generate effective time–frequency images of ground moving targets and thereby enrich the sample database used to train the classification network. Then, image quality evaluation indexes are introduced to evaluate the generated spectrogram samples, with an aim to verify the distribution similarity of generated and real samples. Afterward, by feeding augmented samples to the deep convolutional neural networks with good generalization capacity, the classification performance of the GMT-WGAN is improved. Finally, experiments conducted on different datasets validate the effectiveness and robustness of the proposed method.

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