High-resolution Inverse Synthetic Aperture Radar Imaging with Sparse Stepped-frequency Chirp Signals under Low Signal to Noise Ratio

WANG Yue; BAI Xueru; ZHOU Feng

doi:10.11999/JEIT210056

Volume 44 Issue 3

Mar. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 1034-1043

WANG Yue, BAI Xueru, ZHOU Feng. High-resolution Inverse Synthetic Aperture Radar Imaging with Sparse Stepped-frequency Chirp Signals under Low Signal to Noise Ratio[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1034-1043. doi: 10.11999/JEIT210056

Citation:

WANG Yue, BAI Xueru, ZHOU Feng. High-resolution Inverse Synthetic Aperture Radar Imaging with Sparse Stepped-frequency Chirp Signals under Low Signal to Noise Ratio[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1034-1043. doi: 10.11999/JEIT210056

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High-resolution Inverse Synthetic Aperture Radar Imaging with Sparse Stepped-frequency Chirp Signals under Low Signal to Noise Ratio

doi: 10.11999/JEIT210056

WANG Yue¹,
BAI Xueru²,
ZHOU Feng^{1
,
,}

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

Funds: The National Natural Science Foundation of China (61971332, 61631019)

Received Date: 2021-01-18
Rev Recd Date: 2021-03-31

Available Online: 2021-04-19

Publish Date: 2022-03-28

Abstract

Abstract

To solve the sensitivity of sparse stepped-frequency chirp signals to target radial motion and to achieve high-resolution imaging with low Signal to Noise Ratio (SNR), a translation compensation and high-resolution Inverse Synthetic Aperture Radar (ISAR) imaging based on genetic algorithm and sparse Bayesian learning is proposed. Firstly, an echo model and a sparse observation model are established for the sparse stepped-frequency chirp signal. A parameterized dictionary is then constructed to turn ISAR imaging to the joint estimation of target motion parameter and High-Resolution Range Profile (HRRP) synthesis. Secondly, the Gamma-Gaussian prior is introduced to the high-resolution range profile of the target, and the scattering center is estimated by the Variational Bayesian Inference (VBI) algorithm. On this basis, target motion parameters and high-quality HRRP are obtained through the iteration of genetic algorithm. Hence, high-resolution imaging of the moving targets is achieved while the motion parameters are accurately estimated. The effectiveness of the proposed method is verified by simulation and real data processing result in various scenes.
- Inverse Synthetic Aperture Radar (ISAR),
- Motion parameter estimation,
- Sparse Bayesian learning,
- Genetic algorithm

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References(20)

References

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