Adaptive Off-grid Calibration Method for MIMO Radar 3D Imaging
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摘要: 在压缩感知成像算法中，真实目标点一般不会恰好落在预先划定的网格点上，这种网格偏离(Off-grid)问题会带来真实回波与测量矩阵之间的失配，严重降低雷达成像的性能。针对多输入多输出(MIMO)雷达3维成像的网格失配问题，该文提出一种自适应的Off-grid校正方法，基于Off-grid目标的稀疏回波模型构造贝叶斯概率密度函数，采用最大后验概率(MAP)方法求解含有失配偏差的稀疏像。与传统方法相比，该方法可以充分利用失配参数的先验信息，自适应地更新参数，降低了失配误差的影响，并能实现对稀疏目标和噪声功率的高精度估计。仿真结果表明，该方法可以有效地实现对网格失配的优化，具有精确且稳定的成像性能。
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关键词:
- MIMO雷达 /
- Off-grid校正 /
- 3维稀疏成像 /
- 最大后验概率
Abstract: In Compressive Sensing (CS) imaging algorithms, the true targets usually can not locate on the pre-defined grids exactly. Such Off-grid problems result in mismatch between true echo and measurement matrix, which seriously degrades the performance of radar imaging. An adaptive calibration method is proposed to solve the off-grid problems in MIMO radar Three-Dimensional (3D) imaging. Bayesian probability density functions can be constructed based on the sparse echo model of Off-grid targets, and the Maximum A Posteriori (MAP) method is used to obtain sparse imaging with mismatch errors. Compared with the traditional methods, the proposed method can make full use of mismatch parameters’ priori information and adaptively update the parameters, which can reduce the influence of mismatch errors, and achieve high-precision estimation for sparse targets and noise power. Finally, the simulation results confirm that the proposed method can effectively optimize mismatch errors with accurate and stable imaging performance. -
表 1 计算复杂度对比
方法 计算复杂度 迭代终止次数 OMP方法 O(IOMP×M 2N 2QU) IOMP = 16 SACR-iMAP方法 O(ISACR-iMAP×(3M 2N 2QU+2U 3) ISACR-iMAP = 15 本文方法 O((I1–I0)×I2 (6M 2N 2QU+3U 3)) I1 = 9, I0 = 3, I2 = 7 SAF-BCS方法 O(ISAF-BCS×(15M 2N 2QU+4U 3)) ISAF-BCS = 38 
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保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2010: 24–30. FISHLER E, HAIMOVICH A, BLUM R, et al. MIMO radar: An idea whose time has come[C]. Proceedings of 2004 IEEE Radar Conference, Philadelphia, USA, 2004: 71–78. DUARTE M F and ELDAR Y C. Structured compressed sensing: From theory to applications[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4053–4085. doi: 10.1109/TSP.2011.2161982 谢晓春. 压缩感知理论在雷达成像中的应用研究[D]. [博士论文], 中国科学院空间科学与应用研究中心, 2010.XIE Xiaochun. Study on the appilcation of compressive sensing in radar imaging[D]. [Ph.D. dissertation], The Center for Space Science and Applied Research, Chinese Academy of Sciences, 2010. ZHU Yutao and SU Yi. A type of M2-transmitter N2-receiver MIMO radar array and 3D imaging theory[J]. Science China Information Sciences, 2011, 54(10): 2147–2157. doi: 10.1007/s11432-011-4400-y HU Xiaowei, TONG Ningning, WANG Jianye, et al. Matrix completion-based MIMO radar imaging with sparse planar array[J]. Signal Processing, 2017, 131: 49–57. doi: 10.1016/j.sigpro.2016.07.034 HU Xiaowei, TONG Ninging, ZHANG Yongshun, et al. Multiple-input–multiple-output radar super-resolution three-dimensional imaging based on a dimension-reduction compressive sensing[J]. IET Radar, Sonar & Navigation, 2016, 10(4): 757–764. doi: 10.1049/iet-rsn.2015.0345 DING Shanshan, TONG Ninging, ZHANG Yongshun, et al. Super-resolution 3D imaging in MIMO radar using spectrum estimation theory[J]. IET Radar, Sonar & Navigation, 2017, 11(2): 304–312. doi: 10.1049/iet-rsn.2016.0233 HU Xiaowei, TONG Ningning, SONG Baojun, et al. Joint sparsity-driven three-dimensional imaging method for multiple-input multiple-output radar with sparse antenna array[J]. IET Radar, Sonar & Navigation, 2017, 11(5): 709–720. doi: 10.1049/iet-rsn.2016.0108 CANDÈS E and ROMBERG J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3): 969–985. doi: 10.1088/0266-5611/23/3/008 BAO Qian, HONG Wen, HAN Kuoye, et al. Off-grid effect free imaging method based on improved OMP approach for DLLA 3D SAR[C]. Proceedings of 2015 IET International Radar Conference, Hangzhou, 2015: 1–4. BAO Qian, HAN Kuoye, PENG Xueming, et al. DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization[J]. Science China Information Sciences, 2016, 59(6): 062310. doi: 10.1007/s11432-015-5477-5 LIU Changchang, DING Li, and CHEN Weidong. A correction and generalization to the sparse learning via iterative minimization method for target off the grid in MIMO radar imaging[C]. Proceedings of 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2012: 895–899. HE Xuezhi, LIU Changchang, LIU Bo, et al. Sparse frequency diverse MIMO radar imaging for off-grid target based on adaptive iterative MAP[J]. Remote Sensing, 2013, 5(2): 631–647. doi: 10.3390/rs5020631 丁丽. MIMO雷达稀疏成像的失配问题研究[D]. [博士论文], 中国科学技术大学, 2014.DING Li. Research on observation matrix mismatch for MIMO radar sparse imaging[D]. [Ph.D. dissertation], University of Science and Technology of China, 2014. 王天云, 陆新飞, 丁丽, 等. 基于贝叶斯压缩感知的FD-MIMO雷达Off-Grid目标稀疏成像[J]. 电子学报, 2016, 44(6): 1314–1321. doi: 10.3969/j.issn.0372-2112.2016.06.008WANG Tianyun, LU Xinfei, DING Li, et al. Bayesian compressive sensing-based sparse imaging for Off-Grid target in frequency diverse MIMO radar[J]. Acta Electronica Sinica, 2016, 44(6): 1314–1321. doi: 10.3969/j.issn.0372-2112.2016.06.008 王超宇, 贺亚鹏, 胡恒, 等. 基于贝叶斯压缩感知的噪声MIMO雷达目标成像[J]. 南京理工大学学报, 2013, 37(2): 262–268. doi: 10.3969/j.issn.1005-9830.2013.02.011WANG Chaoyu, HE Yapeng, HU Heng, et al. Noise MIMO radar target imaging based on Bayesian compressive sensing[J]. Journal of Nanjing University of Science and Technology, 2013, 37(2): 262–268. doi: 10.3969/j.issn.1005-9830.2013.02.011 JI Shihao, XUE Ya, and CARIN L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2346–2356. doi: 10.1109/TSP.2007.914345 王伟, 张斌, 李欣. 基于混合匹配追踪算法的MIMO雷达稀疏成像方法[J]. 电子与信息学报, 2016, 38(10): 2415–2422. doi: 10.11999/JEIT151453WANG Wei, ZHANG Bin, and LI Xin. An imaging method for MIMO radar based on hybrid matching pursuit[J]. Journal of Electronics &Information Technology, 2016, 38(10): 2415–2422. doi: 10.11999/JEIT151453 -
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