Trajectory prediction of hypersonic glide vehicle based on empirical wavelet transform and attention convolutional long short-term memory network

2022 ◽  
pp. 1-1
Author(s):  
Junbiao Zhang ◽  
Jiajun Xiong ◽  
Xuhui Lan ◽  
Yanan Shen ◽  
Xin Chen ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Short Term Memory ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Empirical Wavelet Transform ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Vehicle Emission Forecasting Based on Wavelet Transform and Long Short-Term Memory Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 56984-56994 ◽  
Author(s):  
Qiang Zhang ◽  
Feng Li ◽  
Fei Long ◽  
Qiang Ling
Keyword(s):  
Wavelet Transform ◽  
Short Term Memory ◽  
Vehicle Emission ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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