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| Citation: | DENG Likang, ZHANG Shuanghui, ZHANG Chi, et al. A multiple-input multiple-output inverse synthetic aperture radar imaging method based on multidimensional alternating direction method of multipliers[J]. Journal of Radars, 2021, 10(3): 416–431. doi: 10.12000/JR20132
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A Multiple-Input Multiple-Output Inverse Synthetic Aperture Radar Imaging Method Based on Multidimensional Alternating Direction Method of Multipliers
doi: 10.12000/JR20132
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Abstract
The disadvantages of the traditional Inverse Synthetic Aperture Radar (ISAR) imaging method based on Fourier transform include large data storage and long collection time. The Compressive Sensing (CS) theory can use limited data to restore an image with the sparsity of the image, reducing the cost of data collection. However for multidimensional data, the traditional compressive sensing methods need to convert three-dimensional data into a one-dimensional vector, causing the storage and calculation burden. Therefore, this study proposes a fast MultiDimensional Alternating Direction Method of Multipliers ((MD-ADMM)) sparse reconstruction method for Multiple-Input Multiple-Output ISAR (MIMO-ISAR) imaging. The CS model based on the tensor signal was established, and the model with the ADMM algorithm was optimized. The measured matrix is decomposed into a tensor modal product, and matrix inversion is replaced by tensor element division, significantly reducing memory consumption and computational burden. Fast ISAR imaging can be achieved by a small amount of data sampling by the proposed method. Compared with other tensor compressed sensing methods, this method has the advantages of stronger robustness, higher image quality, and computational efficiency. The effectiveness of the proposed method can be invalidated by simulated and measured data. -
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References
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- Figure 1. Geometry of imaging
- Figure 2. The equivalent transceiver array element for linear receiving and transmitting array elements
- Figure 3. Motion of the target
- Figure 4. 3D scatter of simulation target
- Figure 5. Three views of image with the complete echo
- Figure 6. Undersampling masks of random sampling and block sampling
- Figure 7. Image when sparsity is 50.0%
- Figure 8. Image when sparsity is 33.3%
- Figure 9. Image when sparsity is 25.0%
- Figure 10. The image of a sparsity of 50.0% by block sampling
- Figure 11. The image when sparsity is 25.0% and SNR=–5 dB
- Figure 12. The image when sparsity is 25.0% and SNR=0 dB
- Figure 13. The image when sparsity is 25.0% and SNR=10 dB
- Figure 14. Measured ISAR data results
- Figure 15. Measured ISAR data results under SNR=0 dB