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| Citation: | HUANG Zhongling, WU Chong, YAO Xiwen, et al. Physically explainable intelligent perception and application of SAR target characteristics based on time-frequency analysis[J]. Journal of Radars, 2024, 13(2): 331–344. doi: 10.12000/JR23191
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Physically Explainable Intelligent Perception and Application of SAR Target Characteristics Based on Time-frequency Analysis
doi: 10.12000/JR23191
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Abstract
The current state of intelligent target recognition approaches for Synthetic Aperture Radar (SAR) continues to experience challenges owing to their limited robustness, generalizability, and interpretability. Currently, research focuses on comprehending the microwave properties of SAR targets and integrating them with advanced deep learning algorithms to achieve effective and resilient SAR target recognition. The computational complexity of SAR target characteristic-inversion approaches is often considerable, rendering their integration with deep neural networks for achieving real-time predictions in an end-to-end manner challenging. To facilitate the utilization of the physical properties of SAR targets in intelligent recognition tasks, advancing the development of microwave physical property sensing technologies that are efficient, intelligent, and interpretable is imperative. This paper focuses on the nonstationary nature of high-resolution SAR targets and proposes an improved intelligent approach for analyzing target characteristics using time-frequency analysis. This method enhances the processing flow and calculation efficiency, making it more suitable for SAR targets. It is integrated with a deep neural network for SAR target recognition to achieve consistent performance improvement. The proposed approach exhibits robust generalization capabilities and notable computing efficiency, enabling the acquisition of classification outcomes of the SAR target characteristics that are readily interpretable from a physical standpoint. The enhancement in the performance of the target recognition algorithm is comparable to that achieved by the attribute scattering center model. -
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References
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- Figure 1. Intelligent perception and application flow of microwave visual characteristics
- Figure 3. Example of a segmentation algorithm dealing with a T-72 target
- Figure 2. Segmentation algorithm flow
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Figure 4. Schematic diagram of parameters
$ {N_{{\text{rg}}}} $ $ {N_{{\text{az}}}} $ $ {N_{\text{f}}} $ $ {N_{{\text{win}}}} $ - Figure 5. Structure of a PIHA-based network
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Figure 6. Hamming window filter groups at different settings of
${N_{\mathrm{f}}} $ $ {N_{{\mathrm{rg}}}} $ - Figure 7. Scattering characteristics of the target in 2D sub-bands with different parameter settings
- Figure 8. The processing results of the Sobel operator (the red mark represents the artificial target, and the yellow mark represents the background clutter)
- Figure 9. Analysis of the scattering characteristics of the sub-band scattering characteristics of ASC model scattering center simulation results
- Figure 10. Analysis of the sub-band scattering characteristics of different components of the T-72 target (the x-axis andy-axis are azimuth and distance, respectively)
- Figure 11. Scattering of the two-dimensional sub-bands closest to the four clustering centers
- Figure 12. Distribution map of each microwave visual characteristic of T-72 target
- Figure 13. Distribution map of each microwave visual characteristic of BMP target