Distributed ISAR Two-dimensional Imaging of Moving Target with Nonorthogonal Waveforms

LI Yuanyuan; FU Yaowen; ZHANG Wenpeng; YANG Wei

doi:10.11999/JEIT210775

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3541-3552

LI Yuanyuan, FU Yaowen, ZHANG Wenpeng, YANG Wei. Distributed ISAR Two-dimensional Imaging of Moving Target with Nonorthogonal Waveforms[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3541-3552. doi: 10.11999/JEIT210775

Citation:

LI Yuanyuan, FU Yaowen, ZHANG Wenpeng, YANG Wei. Distributed ISAR Two-dimensional Imaging of Moving Target with Nonorthogonal Waveforms[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3541-3552. doi: 10.11999/JEIT210775

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Distributed ISAR Two-dimensional Imaging of Moving Target with Nonorthogonal Waveforms

doi: 10.11999/JEIT210775

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (61901487, 61871384)

Received Date: 2021-08-04
Accepted Date: 2022-03-10
Rev Recd Date: 2022-02-01

Available Online: 2022-03-20

Publish Date: 2022-10-19

Abstract

Abstract

In distributed Inverse Synthetic Aperture Radar (ISAR) imaging, if the transmitted waveforms are nonorthogonal, it is difficult to obtain the ideal range image by the traditional matched filtering method, which will affect the azimuth imaging effect. Sparse-based method can replace matched filtering in range profile separation. In this paper, after describing the sparse representation model of range image in a single snapshot, by adjusting the delay of the transmitting and receiving sensors, the range image with multiple receiving sensors can have joint-block sparse characteristics. Then, a Multiple Measurement Vectors Joint Block (MMV-JBlock) algorithm is constructed using Sequential Order One Negative Exponential (SOONE) function to improve the effect of sparse reconstruction. For multiple snapshots, the MMV-JBlock method is used to separate the range image at each snapshot firstly. After aligning the multi-channel range images, the uninterested directional motion and error items in the azimuth phase are compensated. Finally, the sparse method is used to obtain the target azimuth image. The simulation verifies the reconstruction performance of the proposed algorithm under different sparsity and different signal-to-noise ratios, and achieves the imaging of moving targets by distributed ISAR, which validates the effectiveness of the proposed method.
- Distributed Inverse Synthetic Aperture Radar (ISAR) imaging,
- Nonorthogonal waveforms,
- Sparse recovery

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References(19)

References

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