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| Citation: | YANG Xiaopeng, MA Zhongjie, ZHONG Shichao, et al. Trajectory planning method for UAV-through-the-wall 3D SAR based on a genetic algorithm[J]. Journal of Radars, in press. doi: 10.12000/JR24068
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Trajectory Planning Method for UAV-Through-the-wall 3D SAR Based on a Genetic Algorithm
doi: 10.12000/JR24068
More Information-
Abstract
Due to height limitations, the traditional handheld or vehicle-mounted Through-the-Wall Radar (TWR) cannot provide the perspective imaging of internal targets in urban high-rise buildings. Unmanned Aerial Vehicle-TWR (UAV-TWR) offers flexibility, efficiency, convenience, and no height limitations, allowing for large-scale three-Dimensional (3D) penetration detection of urban high-rise buildings. While the multibaseline scanning mode is widely used in 3D tomographic Synthetic Aperture Radar (SAR) imaging to provide resolution in the altitude direction, it often suffers from the grating lobe problem owing to under-sampling in the altitude spatial domain. Therefore, this paper proposes a trajectory planning algorithm for UAV-through-the-wall 3D SAR imaging based on a genetic algorithm to address this issue. By nonuniformizing flight trajectories, the periodic radar echo energy superposition is weakened, thereby suppressing grating lobes to achieve better imaging quality. The proposed algorithm combines the inherent relationship between the flight distance and TWR imaging quality and establishes a cost function for UAV-TWR trajectory planning. We use the genetic algorithm to encode genes for three typical flight trajectory control points and optimize the population and individuals through gene hybridization and mutation. The optimal flight trajectory for each of the three flight modes is selected by minimizing the cost function. Compared with the traditional equidistant multibaseline flight mode, the imaging results from simulations and measured data show that the proposed algorithm significantly suppresses the grating lobe effect of targets. In addition, oblique UAV flight trajectories are significantly shortened, improving the efficiency of through-the-wall SAR imaging. -
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References
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- Figure 1. Schematic diagram of detection scenario for UAV-TWR in high-rise buildings
- Figure 2. Schematic diagram of grating scene
- Figure 3. UMB-SAR genotype
- Figure 4. CF-SAR genotype
- Figure 5. OF-SAR genotype
- Figure 6. Through-the-wall 3D simulation scene
- Figure 7. MB-SAR trajectory and imaging results
- Figure 8. UMB-SAR trajectory optimization and imaging results
- Figure 9. Z-SAR trajectory optimization and imaging results
- Figure 10. CF-SAR trajectory optimization and imaging results
- Figure 11. OF-SAR trajectory optimization and imaging results
- Figure 12. UAV-TWR CEM200
- Figure 13. Measurement of through-the-wall scene
- Figure 14. Actual measurement of UAV flight trajectory in different modes in through-the-wall scenarios
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Figure 15. Comparison of
$ {R_{\rm{pgl}}} $ - Figure 16. Imaging situation of four modes in through-the-wall scenario (“Ⅰ” represents the range-height section, “Ⅱ” represents the azimuth-height section)