Abnormal Dynamic Recognition of Space Targets from ISAR Image Sequences with SSAE-LSTM Network

Abnormal dynamics awareness of space targets is critical for space surveillance. With the intrinsic range-Doppler projection mechanism, an inverse synthetic aperture radar (ISAR) image sequence naturally has crucial potential for abnormal dynamics interpretation on uncooperative targets. In this pap...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
Main Authors: Duan, Jia, Ma, Yan, Zhang, Lei, Xie, Pengfei
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
abnormal dynamic
Aerospace electronics
Artificial neural networks
Computer architecture
Doppler sonar
Dynamics
Feature extraction
Image sequences
Image sequencing
Imaging
Inverse synthetic aperture radar
ISAR sequences
LSTM
Neural networks
Radar imaging
SAR (radar)
Sequencing
Space target
Space vehicles
SSAE
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Abnormal dynamics awareness of space targets is critical for space surveillance. With the intrinsic range-Doppler projection mechanism, an inverse synthetic aperture radar (ISAR) image sequence naturally has crucial potential for abnormal dynamics interpretation on uncooperative targets. In this paper, we develop an automated deep neural network architecture for abnormal dynamic recognition of space targets from ISAR image sequences. With the accommodation of the stacked sparse autoencoder (SSAE) network joint sparsity constraint, the geometrical feature flow of an ISAR image sequence is learned to represent the target dynamics concisely. Then, the abnormal dynamic recognition is turned into a sequential classification task by exploiting the encoded feature flow through the long-short time memory (LSTM) network. Extensive experiment results confirm the superiority of the proposal in both precision and efficiency aspects.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3264995