Fusion Recognition of Space Targets with Micro-Motion Based on a Sparse Auto-Encoder

TIAN Xudong; BAI Xueru; ZHOU Feng

doi:10.11999/JEIT221163

Volume 45 Issue 12

Dec. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(12): 4336-4344

TIAN Xudong, BAI Xueru, ZHOU Feng. Fusion Recognition of Space Targets with Micro-Motion Based on a Sparse Auto-Encoder[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4336-4344. doi: 10.11999/JEIT221163

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TIAN Xudong, BAI Xueru, ZHOU Feng. Fusion Recognition of Space Targets with Micro-Motion Based on a Sparse Auto-Encoder[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4336-4344. doi: 10.11999/JEIT221163

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Fusion Recognition of Space Targets with Micro-Motion Based on a Sparse Auto-Encoder

doi: 10.11999/JEIT221163

1.
Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi’an 710071, China
2.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62131020), The Fundamental Research Funds for the Central Universities

Received Date: 2022-09-06
Rev Recd Date: 2023-03-10

Available Online: 2023-03-16

Publish Date: 2023-12-26

Abstract

Abstract

During the observation of micro-motion targets in space, high resolution radar collects the narrowband and wideband echoes simultaneously. This paper proposes a fusion method based on a Sparse Auto-Encoder (SAE) for recognizing space micro-motion targets to exploit their rich electromagnetic scattering, shape, structure, and motion information. In the training phase, the proposed method extracts the hierarchical features from High Resolution Range Profiles (HRRP), Joint Time-Frequency (JTF) images, and Range-Instantaneous-Doppler (RID) images using Convolution Neural Networks (CNN). The joint feature vector is then created by concatenating the relevant deep features, and SAE learns autonomously its hidden features unsupervised. After that, the decoder is removed and the Softmax classifier is introduced after the encoder to create the recognition network. Finally, parameters of the optimized SAE network are used for the initialization of the recognition network, which is then fine-tuned by the joint feature vectors of training samples. In the test phase, the trained recognition network is supplied directly with the joint feature vectors of the test samples recovered by CNN to produce the fusion recognition results. Experimental results of simulated EM data under different conditions show the efficacy and robustness of the proposed method.
- Micro-motion space targets,
- Fusion recognition,
- Convolution Neural Network (CNN),
- Sparse Auto-Encoder (SAE)

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References(22)

References

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