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Synthetic Aperture Radar Learning-imaging Method Based onData-driven Technique and Artificial Intelligence
doi: 10.12000/JR19103
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Abstract
One of the most important research fields in Synthetic Aperture Radar (SAR) technology is to improve the accuracies of the number, location, classification, and other parameters of targets of interest. SAR information processing can be mainly divided into two tasks: imaging and interpretation. At present, research efforts on these two tasks are relatively independent. Many algorithms have been developed for SAR imaging and interpretation, and they have become increasingly complex. However, SAR interpretation has not been made simpler by improvements in the imaging resolution. The low recognition rate of key targets, in particular, has yet to be adequately resolved. In this paper, we use a “data driven + intelligence learning” method to improve the information processing ability of airborne SAR based on SAR imaging & interpretation integration. First, we analyze the feasibility and main problems of SAR imaging & interpretation integration using a “data driven + intelligence learning” method. Based on the results, we propose a SAR learning-imaging method based on “data driven + intelligence learning” with the goal of producing an imaging network. The proposed learning-imaging framework, parameter selection method, network training method, and preliminary simulation results are presented, and the key technical problems to be solved are identified and analyzed. -
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References
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- Figure 1. Basic flow chart of SAR information processing at present
- Figure 2. An flow chart of SAR imaging & interpretation integration from “echo data domain” to “target parameter domain”
- Figure 3. One dimensional SAR observation model
- Figure 4. Network structure of SAR learning-imaging
- Figure 5. Comparison of imaging results using unsupervised training, γ=1 and SNR=20 dB
- Figure 6. Comparison of imaging results using unsupervised training, γ=0.1 and SNR=5 dB
- Figure 7. Imaging results under low PRF condition
- Figure 8. Network training error corresponding to differentnumber of training samples
- Figure 9. Imaging results with platform trajectory error
- Figure 10. MSTAR target imaging results
- Figure 11. Imaging results of two different targets in MSTAR dataset