基于空频分解信号子空间向量的时间反演成像

钟选明; 李军野; 廖成

doi:10.11999/JEIT160272

基于空频分解信号子空间向量的时间反演成像

doi: 10.11999/JEIT160272

基金项目:

国家自然科学基金委和中国工程物理研究院联合基金(U1330109)

计量
- 文章访问数: 1113
- HTML全文浏览量: 107
- PDF下载量: 479
- 被引次数: 0
出版历程
- 收稿日期: 2016-03-21
- 修回日期: 2016-08-17
- 刊出日期: 2017-02-19

Time Reversal Imaging Algorithm Based on Signal-subspaceVectors from the Spatial-frequency Decomposition

Funds:

The United Fund of National Natural Science Foundation of China and China Academy of Engineering Physics (U1330109)

摘要

摘要: 论文提出基于空频分解信号子空间向量的时间反演成像新方法。利用天线阵列采集的散射场数据建立空频矩阵，奇异值分解该矩阵得到信号子空间向量，以此实现对目标的选择性成像。基于完全散射场数据的成像伪谱包含多个子向量贡献，相当于多次成像叠加，具有统计特性。新方法既避免了传统的空空分解时间反演算法产生的随机相位的影响，又具有较好的抗干扰性能，即使叠加信噪比10 dB的高斯白噪声，也能实现对多个目标的准确成像。
- 时间反演成像 /
- 空频成像 /
- 空频分解 /
- 空频多态响应矩阵
Abstract: Basing on the signal-subspace vectors from the spatial-frequency decomposition, a novel time-reversal imaging algorithm is proposed. Using the backscattered data recorded by the antenna array, a spatial-frequency multistatic matrix is set up. Singular value decomposition is applied to the matrix to obtain the signal-subspace vectors, which are employed to focus the targets imaging selectively. The imaging pseudo-spectrum based on the full backscattered data includes the contributions of multiple sub-vectors and can be viewed as the superposition of multiple images. The algorithm is statistically stable. The random phases, generated by the conventional time-reversal imaging method based on the spatial-spatial decomposition, do not arise in the algorithm. It has excellent capability to resist the noise interference and can accurately focus the multi-targets even when noise with 10 dB SNR is added to the measured data.
- Time-reversal imaging /
- Spatial-frequency imaging /
- Spatial-frequency decomposition /
- Spatial-frequency multistatic matrix

HTML全文

参考文献(16)

YAVUZ M E and TEIXEIRA F L. A numerical study of time reversed UWB electromagnetic waves in continuous random media[J]. IEEE Antennas and Wireless Propagation Letters, 2005, 4(6): 43-46. doi: 10.1109/LAWP.2005.844117.

DEVANEY A J. Time reversal imaging of obscured targets from multistatic data[J]. IEEE Transactions on Antennas Propagation, 2005, 53(5): 1600-1610. doi: 10.1109/TAP. 2005. 846723.

范晶晶, 赵德双, 张浩然, 等. 基于时间反演的天线阵列激励分布确定方法研究[J]. 电子与信息学报, 2014, 36(9): 2238-2243. doi: 10.3724/SP.J.1146.2013.01737.

FAN Jingjing, ZHAO Deshuang, ZHANG Haoran, et al. Array excitation determining method based on time reversal [J]. Journal of Electronics Information Technology, 2014, 36(9): 2238-2243. doi: 10.3724/SP.J.1146.2013.01737.

PRADA C, MANNEVILE S, SPOLIANSKY D, et al. Decomposition of the time reversal operator: Detection and selective focusing on two scatterers[J]. Journal of the Acoustical Society of America, 1996, 99(4): 2067-2076.

YAVUZ M E and TEIXEIRA F L. Full time-domain DORT for ultrawideband fields in dispersive, random inhomogeneous media[J]. IEEE Transactions on Antennas Propagation, 2006, 54(8): 2305-2315. doi: 10.1109/TAP.2006.879196.

KAFAL M, COZZA A, and PICHON L. Locating multiple soft faults in wire networks using an alternative DORT implementation[J]. IEEE Transactions on Instrumentation Measurement, 2016, 65(2): 399-406. doi: 10.1109/TIM.2015. 2498559.

GELAT P, HAAR G T, and SAFFARI N. An assessment of the DORT method on simple scatterers using boundary element modelling[J]. Physics in Medicine and Biology, 2015, 60(9): 3715-3730. doi: 10.1088/0031-9155/60/9/3715.

LEV-ARI H and DEVANEY A J. The time reversal techniques reinterpreted: subspace-based signal processing for multistatic target location[C]. Sensor Array Multichannel Signal Processing Workshop, Cambridge, MA, USA, 2000: 509513. doi: 10.1109/SAM.2000.878061.

YAVUZ M E and TEIXEIRA F L. On the sensitivity of time-reversal imaging techniques to model perturbations[J]. IEEE Transactions on Antennas Propagation, 2008, 56(3): 834-843. doi: 10.1109/TAP.2008.916933.

ISLAM M S and KAABOUCH N. Evaluation of TR-MUSIC algorithm efficiency in detecting breast microcalcifications[C]. IEEE International Conference on Electro/Information Technology, DeKalb, IL, USA, 2015: 617-620. doi: 10.1109/EIT. 2015.7293406.

He J and YUAN F G. Lamb waves based fast subwavelength imaging using a DORT-MUSIC algorithm[C]. American Institute of Physics Conference Series, Minneapolis, MN, USA, 2016,1706(1): 1103-1113.

BORCEA L. Interferometric imaging and time reversal in random media[J]. Inverse Problem, 2003, 18(5): 1247-1279. doi: 10.1007/978-3-540-70529-1_157.

SCHOLZ B. Towards virtual electrical breast biopsy: Space-frequency MUSIC for trans-admittance data[J]. IEEE Transactions on Medical Imaging, 2002, 21(6): 588-595. doi: 10.1109/TMI.2002.800609.

PETROLIS R, RAMONAIT R, JANCIAUSKAS D, et al. Digital imaging of colon tissue: Method for evaluation of inflammation severity by spatial frequency features of the histological images[J]. Diagnostic Pathology, 2015, 10(1): 1-10. doi: 10.1186/s13000-015-0389-7.

CUCCIA D J, BEVILACQUA F, DURKIN A J, et al. Modulated imaging: Quantitative analysis and tomography of turbid media in the spatial-frequency domain[J]. Optics Letters, 2005,30(11): 1354-1356. doi: 10.1364/OL.30.001354.

施引文献

资源附件(0)

访问统计