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| Citation: | YUAN Yubing, YE Shengbo, JI Yicai, et al. Fast refocusing algorithm based on three-dimensional wall compensation[J]. Journal of Radars, 2024, 13(4): 822–837. doi: 10.12000/JR24051
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Fast Refocusing Algorithm Based on Three-dimensional Wall Compensation
DOI: 10.12000/JR24051
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Abstract
Ultra-wideband through-wall radar, leveraging its ability to penetrate walls, can be used together with Multiple-Input Multiple-Output (MIMO) technology to image hidden targets behind walls. This approach provides rich information for detecting and locating people within buildings. This paper introduces a closed-loop interferometric calibration method based on a multitransmitter multireceiver ultrawideband wall-penetrating radar system in the Frequency Modulated Continuous Wave (FMCW) regime. This method aims to correct scattering issues caused by internal system errors. The presence of walls causes the target imaging position to deviate from the real position. To address this, this paper derives a three-Dimensional (3D) wall compensation algorithm jointing channels and pixel points. Then, a fast refocusing algorithm is proposed based on the geometric properties of the imaging area. The first step involves removing the influence of walls on delay time and determining the presence of the target. Subsequently, in view of the geometric properties of the region, a spherical coordinate grid division adapted to the region shape is selected. Localized refocusing is then performed in the subregion. This avoids the issue of electromagnetic wave attenuation, causing strong targets to mask weak ones in the imaging results. At the same time, the adoption of spherical coordinates for gridding and localized imaging greatly reduces the overall time consumption by the algorithm. Through simulation analysis and experimental verification, the proposed calibration method can effectively compensate for system errors. The fast refocusing algorithm can be used to realize multitarget 3D localization of the human body behind walls, with the localization accuracy of each dimension surpassing 10 cm and computational speeds improving by five times compared with those of existing algorithms. In terms of target detection probability, the proposed algorithm consistently identifies weak targets that other algorithms may overlook. -
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References
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- Figure 1. The block diagram of MIMO radar system
- Figure 2. The physical structure of the antenna array
- Figure 3. The corner reflector imaging scenario
- Figure 4. 3D imaging results of the corner reflector before and after calibration
- Figure 5. Flowchart of the proposed refocusing algorithm
- Figure 6. 2D propagation path through the wall
- Figure 7. 3D wall penetrating model
- Figure 8. 3D propagation path through the wall
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Figure 9. The variation of
$\delta R$ $\theta $ - Figure 10. Local imaging in 3D space
- Figure 11. Grid partitioning with regional focus
- Figure 12. The imaging results of traditional algorithm
- Figure 13. The simulation imaging results of the proposed algorithm
- Figure 14. Target positioning error curve
- Figure 15. The experimental scenario of two human targets
- Figure 16. Center channel echo range-slow time distribution
- Figure 17. The imaging results of two human targets using the proposed algorithm
- Figure 18. The 3D imaging results of two human targets using various algorithms
- Figure 19. The experimental scenario of three human targets
- Figure 20. Center channel echo range-slow time distribution of three human targets
- Figure 21. The imaging results of three human targets using the proposed algorithm
- Figure 22. The 3D imaging results of three human targets using various algorithms
- Figure 23. Narrow space experimental scene
- Figure 24. Imaging results of various methods in a narrow spaces