High-resolution 3D Imaging via Wideband MIMO Radar Based on Kronecker Compressive Sensing

HU Xiaowei; TONG Ningning; HE Xingyu; DING Shanshan; LEI Teng

doi:10.11999/JEIT150995

Volume 38 Issue 6

Jun. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(6): 1475-1481

HU Xiaowei, TONG Ningning, HE Xingyu, DING Shanshan, LEI Teng. High-resolution 3D Imaging via Wideband MIMO Radar Based on Kronecker Compressive Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1475-1481. doi: 10.11999/JEIT150995

Citation:

HU Xiaowei, TONG Ningning, HE Xingyu, DING Shanshan, LEI Teng. High-resolution 3D Imaging via Wideband MIMO Radar Based on Kronecker Compressive Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1475-1481. doi: 10.11999/JEIT150995

Citation:

PDF( 1945 KB)

High-resolution 3D Imaging via Wideband MIMO Radar Based on Kronecker Compressive Sensing

doi: 10.11999/JEIT150995

1.
(Air and Missile Defense College, Air Force Engineering University, Xi&rsquo
2.
(Unit 95899 of PLA, Jiuquan 735018, China)

Funds:

The National Natural Science Foundation of China (61372166, 61571459), The Natural Science Basic Research Plan in Shaanxi Province of China (2014JM8308)

Received Date: 2015-09-08
Rev Recd Date: 2016-01-20
Publish Date: 2016-06-19

Abstract

Abstract

In the Three Dimension (3D) imaging using a wideband Multiple-Input Multiple-Output (MIMO) radar, the resolution in the two cross-range dimensions is usually not satisfactory in practice, limited by the length of the MIMO radar array. In the paper, the Compressive Sensing (CS) theory is applied to realize the super resolution in the two cross-range dimensions. Firstly, a joint two dimensions super resolution method via Kronecker CS (KCS) is proposed, to avoid losing the coupling information among different dimensions, which will happen when the super resolution is just considered in each dimension separately. Then, in order to solve the problem of huge storing and computing burden in KCS, a dimension reduction method is proposed further by utilizing the prior information of the low resolution 3D image. Finally, the validity of the method is verified with simulated data and real measured data experiments.
- Wideband MIMO radar,
- Kronecker Compressive Sensing (KCS),
- One single snapshot imaging,
- High- resolution three dimension imaging

FullText(HTML)

References(19)

References

张榆红, 邢孟道, 徐刚. 基于稀疏孔径的联合稀疏约束干涉ISAR机动目标3维成像[J]. 电子与信息学报, 2015, 37(9): 2151-2157. doi: 10.11000/JEIT150125.

ZHANG Yuhong, XING Mengdao, and XU Gang. Joint sparsity constraint interferometric ISAR imaging for 3-D geometry of maneuvering targets with sparse apertures[J]. Journal of Electronics Information Technology, 2015, 37(9): 2151-2157. doi: 10.11000/JEIT150125.

YANG J C, SU W M, and GU H. 3D imaging using narrowband bistatic MIMO radar[J]. Electronics Letters, 2014, 50(15): 1090-1092.

MA C Z, YEO T S, TAN C S, et al. Three-dimensional imaging of targets using colocated MIMO radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8): 3009-3021.

朱宇涛, 粟毅. 一种M2发N2收MIMO雷达阵列及其3维成像方法[J]. 中国科学: 信息科学, 2011, 41(12): 1495-1506.

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, 41(12): 1495-1506.

ZHANG Xiaohua, BAI Ting, MENG Hongyun, et al. Compressive sensing-based ISAR imaging via the combination of the sparsity and nonlocal total variation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(5): 990-994.

WANG Wei, PAN Xiaoyi, LIU Yongcai, et al. Sub-nyquist sampling jamming against ISAR with compressive sensing[J]. IEEE Sensors Journal, 2014, 14(9): 3131-3136.

LIU Hongchao, JIU Bo, LIU Hongwei, et al. A novel ISAR imaging algorithm for micromotion targets based on multiple sparse bayesian learning[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1772-1776.

WANG Lu, ZHAO Lifan, BI Guoan, et al. Enhanced ISAR imaging by exploiting the continuity of the target scene[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5736-5750.

LIU H C, JIU B, LIU H W, et al. Superresolution ISAR imaging based on sparse bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5005-5013.

GU F F, CHI L, ZHANG Q, et al. Single snapshot imaging method in multiple-input multiple-output radar with sparse antenna array[J]. IET Radar, Sonar Navigation, 2013, 7(5): 535-543.

MA C Z, YEO T S, ZHAO Y B, et al. MIMO radar 3D imaging based on combined amplitude and total variation cost function with sequential order one negative exponential form[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2168-2183.

DUARTE M F and BARANIUK R G. Kronecker compressive sensing[J]. IEEE Transactions on Image Processing, 2012, 21(2): 494-504.

孟藏珍, 许稼, 王力宝, 等. 基于Clean 处理的MIMO-SAR正交波形分离[J]. 电子与信息学报, 2013, 35(12): 2809-2814. doi: 10.3724/SP.J.1146.2013.00311.

MENG Cangzhen, XU Jia, WANG Libao, et al. An orthogonal waveform separation method based on clean processing in MIMO-SAR[J]. Journal of Electronics Information Technology, 2013, 35(12): 2809-2814. doi: 10. 3724/SP.J.1146.2013.00311.

BELLETTINI A and PINTO M A. Theoretical accuracy of synthetic aperture sonar micronavigation using a displaced phase center antenna[J]. IEEE Journal of Oceanic Engineering, 2002, 27(4): 780-789.

CANDES E, ROMBERG J, and TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.

DENG H. Polyphase code design for orthogonal netted radar systems[J]. IEEE Transactions on Signal Processing, 2004, 52(11): 3126-3135.

Relative Articles

Supplements(0)

Cited By

Proportional views