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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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 61; s. 1
Hlavní autori: Duan, Jia, Ma, Yan, Zhang, Lei, Xie, Pengfei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3264995