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Forward-looking Imaging Algorithm Based on CNN-LSTM Neural Network
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Xiaohan SUN1, Lianghai LI2, Bin ZHANG1
Journal of Telemetry, Tracking and Command | 2024, 45(2) : 29 - 36
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Journal of Telemetry, Tracking and Command | 2024, 45(2): 29-36
• Artificial Intelligence Technology •
Forward-looking Imaging Algorithm Based on CNN-LSTM Neural Network
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Xiaohan SUN1, Lianghai LI2, Bin ZHANG1
Affiliations
  • 1.Beijing Research Institute of Telemetry, Beijing 100076, China
  • 2.China Academy of Aerospace Electronics Technology, Beijing 100080, China
doi: 10.12347/j.ycyk.20231225001
Outline
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As a difficulty and focus in the field of radar imaging, radar forward-looking imaging has broad application prospects in automatic driving, navigation, precision guidance and so on. The traditional forward-looking imaging algorithm is limited by the width of the antenna aperture and cannot achieve high-resolution imaging. In this paper, CNN ( Convolutional Neural Networks )neural network and LSTM ( Long Short-Term Memory ) neural network are combined to realize the prediction of azimuth in forward-looking imaging. Firstly, the convolution-like model of the scanning forward-looking imaging signal and its ill-posedness are introduced. The echo signal is preprocessed by pulse compression and range migration correction, and input into the CNN-LSTM neural network to perform azimuth estimation by range unit. The simulation results show that the algorithm can effectively improve the azimuth resolution of forward-looking imaging and realize the super-resolution of forward-looking imaging.

Forward-looking imaging  /  Deep learning  /  Convolutional neural network  /  Ill-posed inverse problem
Xiaohan SUN, Lianghai LI, Bin ZHANG. Forward-looking Imaging Algorithm Based on CNN-LSTM Neural Network[J]. Journal of Telemetry, Tracking and Command, 2024 , 45 (2) : 29 -36 . DOI: 10.12347/j.ycyk.20231225001
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Article Info
doi: 10.12347/j.ycyk.20231225001
  • Receive Date：2023-12-25
  • Online Date：2026-03-18
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History
  • Received：2023-12-25
  • Revised：2024-01-03
Affiliations
    1.Beijing Research Institute of Telemetry, Beijing 100076, China
    2.China Academy of Aerospace Electronics Technology, Beijing 100080, China
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https://castjournals.cast.org.cn/joweb/ycyk/EN/10.12347/j.ycyk.20231225001
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