3D Radar Imaging Based on Target Scenario Structer Sparse Reconstruction
-
摘要: 基于成像场景散射强度稀疏表示的3维雷达成像结果对目标的外形几何细节体现较差，不利于目标识别。该文首先分析了目标在成像场景内散射强度的结构化特征，然后以散射点梯度信息进行了结构化稀疏表示，构建了基于目标散射强度梯度变化的结构化稀疏重构模型，最后通过改进的联合正交匹配追踪算法重构出目标3维图像。实验结果表明，该方法具有较好的抗噪性能和成像质量，可以更好地反映目标外形几何特征。Abstract: The three-Dimensional (3D) radar imaging mathods based on sparse representation by the scattering intensity of imaging sceen has a poor representation of geometric details on the shape of the target, which isn’t conducive to target recognition. Firstly, the structural characteristics of scattering intensity in the imaging scenario are analyzed in this paper. Then, by the structured sparse representation with the gradient information of scattering points, a structured sparse reconstruction model is constructed. Finally, the 3D imaging result is reconstructed by a improved joint Orthogonal Matching Pursuit (OMP) algorithm. The experimental results show that the proposed method has good anti-noise and imaging quality, and can reflect the geometric details of the target.
-
表 1 算法性能对比
OMP 正则化${\ell _1}$ 本文所提方法 迭代次数 12 120 74 距离分辨率(m) 0.162 0.158 0.164 方位/高度分辨率(m) 0.142 0.140 0.146 MSE 2.87e-4 8.36e-5 4.76e-6 SSIM 0.673 0.831 0.912 
下载: 导出CSV
-
洪文, 王彦平, 林赟, 等. 新体制SAR三维成像技术研究进展[J]. 雷达学报, 2018, 7(6): 633–654. doi: 10.12000/JR18109HONG Wen, WANG Yanping, LIN Yun, et al. Research progress on three-dimensional SAR imaging techniques[J]. Journal of Radars, 2018, 7(6): 633–654. doi: 10.12000/JR18109 张晓玲, 师君, 韦顺君, 等. 三维合成孔径雷达[M]. 北京: 国防工业出版社, 2017: 1–17, 81–148.ZHANG Xiaoling, SHI Jun, WEI Shunjun, et al. Three Dimensional Synthetic Aperture Radar[M] Beijing: National Defense Industry Press, 2017: 1–17, 81–148. 刘新, 阎焜, 杨光耀, 等. UWB-MIMO穿墙雷达三维成像与运动补偿算法研究[J]. 电子与信息学报, 2020, 42(9): 2253–2260. doi: 10.11999/JEIT190356LIU Xin, YAN Kun, YANG Guangyao, et al. Study on 3D imaging and motion compensation algorithm for UWB-MIMO through-wall radar[J]. Journal of Electronics &Information Technology, 2020, 42(9): 2253–2260. doi: 10.11999/JEIT190356 王伟, 胡子英, 龚琳舒. MIMO雷达三维成像自适应Off-grid校正方法[J]. 电子与信息学报, 2019, 41(6): 1294–1301. doi: 10.11999/JEIT180145WANG Wei, HU Ziying, and GONG Linshu. Adaptive off-grid calibration method for MIMO radar 3D imaging[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1294–1301. doi: 10.11999/JEIT180145 杨俊刚, 黄晓涛, 金添. 压缩感知雷达成像[M]. 北京: 科学出版社, 2014: 1–29.YANG Jungang, HUANG Xiaotao, and JIN Tian. Compressed Sensing Radar Imaging[M]. Beijing: Science Press, 2014: 1–29. 田鹤, 于海锋, 朱宇, 等. 基于频域稀疏压缩感知的星载SAR稀疏重航过3维成像[J]. 电子与信息学报, 2020, 42(8): 2021–2028. doi: 10.11999/JEJT190638TIAN He, YU Haifeng, ZHU Yu, et al. Sparse flight 3-D imaging of spaceborne SAR based on frequency domain sparse compressed sensing[J]. Journal of Electronics &Information Technology, 2020, 42(8): 2021–2028. doi: 10.11999/JEJT190638 LOPEZ-SANCHEZ J M and FORTUNY-GUASCH J. 3-D radar imaging using range migration techniques[J]. IEEE Transactions on Antennas and Propagation, 2000, 48(5): 728–737. doi: 10.1109/8.855491 DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582 BARANIUK R and STEEGHS P. Compressive radar imaging[C]. Proceedings of 2007 IEEE Radar Conference, Boston, USA, 2007: 128–133. doi: 10.1109/RADAR.2007.374203. ZHANG Siqian, DONG Ganggang, and KUANG Gangyao. Matrix completion for downward-looking 3-D SAR imaging with a random sparse linear array[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 1994–2006. doi: 10.1109/TGRS.2017.2771826 HU Xiaowei, TONG Ningning, GUO Yiduo, et al. MIMO radar 3-D imaging based on multi-dimensional sparse recovery and signal support prior information[J]. IEEE Sensors Journal, 2018, 18(8): 3152–3162. doi: 10.1109/JSEN.2018.2810705 YANIK M E and TORLAK M. Near-field MIMO-SAR millimeter-wave imaging with sparsely sampled aperture data[J]. IEEE Access, 2019, 7: 31801–31819. doi: 10.1109/ACCESS.2019.2902859 CHU Y J and MAK C M. A new QR decomposition-based RLS algorithm using the split Bregman method for L1-regularized problems[J]. Signal Processing, 2016, 128: 303–308. doi: 10.1016/j.sigpro.2016.04.013 徐宗本, 吴一戎, 张冰尘, 等. 基于L1/2正则化理论的稀疏雷达成像[J]. 科学通报, 2018, 63(14): 1307–1319. doi: 10.1360/N972018-00372XU Zongben, WU Yirong, ZHANG Bingchen, et al. Sparse radar imaging based on L1/2 regularization theory[J]. Chinese Science Bulletin, 2018, 63(14): 1307–1319. doi: 10.1360/N972018-00372 YANG Zengli and ZHENG Y R. A comparative study of compressed sensing approaches for 3-D synthetic aperture radar image reconstruction[J]. Digital Signal Processing, 2014, 32: 24–33. doi: 10.1016/j.dsp.2014.05.016 ZAMANI H and FAKHARZADEH M. 1.5-D sparse array for millimeter-wave imaging based on compressive sensing techniques[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(4): 2008–2015. doi: 10.1109/TAP.2018.2800531 -
图(7) / 表(1)
计量
- 文章访问数: 1318
- HTML全文浏览量: 264
- PDF下载量: 95
- 被引次数: 0
下载:
