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| Citation: | CAI Xiang, WEI Shunjun, WEN Yanbo, et al. Precise reconstruction method for hidden targets based on non-line-of-sight radar 3D imaging[J]. Journal of Radars, 2024, 13(4): 791–806. doi: 10.12000/JR24060
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Precise Reconstruction Method for Hidden Targets Based on Non-line-of-sight Radar 3D Imaging
DOI: 10.12000/JR24060
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Abstract
Non-Line-Of-Sight (NLOS) 3D imaging radar is an emerging technology that utilizes multipath scattering echoes to detect hidden targets. However, this technology faces challenges such as the separation of multipath echoes, reduction of aperture occlusion, and phase errors of reflective surfaces, which hinder the high-precision imaging of hidden targets when using traditional Line-Of-Sight (LOS) radar imaging methods. To address these challenges, this paper proposes a precise imaging method for NLOS hidden targets based on Sparse Iterative Reconstruction (NSIR). In this method, we first establish a multipath signal model for NLOS millimeter-wave 3D imaging radar. By exploiting the characteristics of LOS/NLOS echoes, we extract the multipath echoes from hidden targets using a model-driven approach to realize the separation of LOS/NLOS echo signals. Second, we formulate a total variation multiconstraint optimization problem for reconstructing hidden targets, integrating multipath reflective surface phase errors. Using the split Bregman Total Variation (TV) regularization operator and the phase error estimation criterion based on the minimum mean square error, we jointly solve the multiconstraint optimization problem. This approach facilitates precise imaging and contour reconstruction of NLOS targets. Finally, we construct a planar scanning 3D imaging radar experimental platform and conduct experimental verification of targets such as knives and iron racks in a corner NLOS scenario. Results validate the capability of NLOS millimeter-wave 3D imaging radar in detecting hidden targets and the effectiveness of the method proposed in this paper. -
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References
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- Figure 1. NLOS radar 3D imaging geometry model
- Figure 2. Echo extraction and NSIR reconstruction process
- Figure 3. Experimental platform and experimental scenes
- Figure 4. 2D imaging of experimental scenes
- Figure 5. Results of range compression
- Figure 6. Multipath echo extraction results
- Figure 7. Comparison of the NLOS and LOS imaging results of the metal knives and the metal flower stands (the first line is the NLOS imaging result, the second line is the LOS imaging result, the sampling rate is 100%)
- Figure 8. Comparison of NLOS metal knives imaging results of four algorithms with different sampling rates (from left to right: sampling rates of 100%, 70%, 50%, and 30%, respectively)
- Figure 9. Comparison of NLOS metal flower stands imaging results of four algorithms with different sampling rates (from left to right: sampling rates of 100%, 70%, 50%, and 30%, respectively)
- Figure 10. At the full sampling rate, the maximum projection results of the four algorithms are compared (from left to right, corresponding to BP, RMA, ISTA and NSIR)