基于GTD模型的多雷达信号二维融合

叶钒; 何峰; 张永胜; 董臻

doi:10.3724/SP.J.1146.2010.00278

基于GTD模型的多雷达信号二维融合

doi: 10.3724/SP.J.1146.2010.00278

叶钒^{* 何峰,},
何峰,
张永胜,
董臻

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出版历程
- 收稿日期: 2010-03-24
- 修回日期: 2010-06-14
- 刊出日期: 2011-01-19

Two-dimensional Fusion of Multi-radar Signals Based on GTD Model

摘要

摘要: 多雷达信号2维融合是一种能显著提高成像分辨率和图像质量的参数化成像新方法。但是在宽带小角度观测的情况下，实际目标散射是随着频率的变化而变化，因此传统先插值后处理的融合方法就不再适用了。该文针对以上情况，提出了一种基于几何绕射模型的多雷达信号2维融合的方法，将多雷达信号2维融合问题转化为信号稀疏表示问题，并利用正则化的方法来估计散射模型参数。此方法不仅不需要2维解耦处理，而且通过信号稀疏表示方法可以准确地估计目标散射的频率依赖因子。仿真实验也表明该文方法有效性。
- 多雷达2维信号融合 /
- 信号稀疏表示 /
- 几何绕射模型 /
- 2维解耦
Abstract: Two-dimensional fusion of multi-radar signals is a new parameterized imaging method. It can remarkably improve image resolution. In the case of wide bandwidth and small angle observation, scattering of target dependents on frequency. Traditional fusion methods are inapplicability to this case. A new fusion method of multi-radar signals based on GTD model is proposed in this paper, and signal fusion is transformed into a signal sparse representation problem. Then regularization method is used to estimate parameters of scattering model. It dispenses with the step of two-dimensional decoupling, and estimates frequency-dependent factors of target scattering accurately by signal sparse representation. The experiments also show the validity of the method.
- Two-dimensional fusion of multi-radar signals /
- Signal sparse representation /
- GTD (Geometrical Theory of Diffraction) model /
- Two-dimensional decoupling

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参考文献(1)

Vann L D and Cuomo K M. Multisensor fusion processing for enhanced radar imaging[R]. Lexington, Massachusetts, United States, Lincoln Laboratory, 2000: 1-73.[2]Piou J E, Cuomo K M, and Mayhan J T. A state-space technique for ultrawide- bandwidth coherent processing[R]. Technical Rep. MIT Lincoln Laboratory, TR-1054, 1999.[3]叶钒, 何峰, 梁甸农等. 综合基追踪和AR模型内插的多频带数据融合[C]. CCSP09, 长沙, 2009: 519-521.[4]Ye Fan, He Feng, and Liang Dian-nong, et al.. Multi-band data fusion combining basis pursuit and AR model interpolation[C]. CCSP09, Changsha, 2009: 519-521.[5]王成,胡卫东,郁文贤. 基于非平稳时间序列处理的雷达信号融合[J]. 信号处理,2005, 21(4): 338-343.Wang Cheng, Hu Wei-dong, and Yu Wen-xian. Radar signal fusion based on non-stationary time series processes[J]. Signal Processing, 2005, 21(4): 338-343.[6]杜小勇,胡卫东,郁文贤. 基于稀疏成分分析的逆合成孔径雷达成像技术[J]. 电子学报, 2006, 34(3): 491-495.Du Xiao-yong, Hu Wei-dong, and Yu Wen-xian. ISAR imaging techniques based on sparse component analysis[J]. Acta Electronica Sinica, 2006, 34(3): 491-495.[7]徐少坤,李亚楠,贾宇平,付耀文. 基于矩阵束算法的多雷达融合超分辨成[J]. 信号处理,2008, 42(5): 859-862.Xu Shao-kun, Li Ya-nan, Jia Yu-ping, and Fu Yao-wen. Multi-radar echoes fusion for super-resolution imaging based on matrix pencil algorithm [J]. Signal Processing, 2008, 42(5): 859-862.[8]王菁,周建江,汪飞. 基于GTD模型的目标二维散射中心提取[J].电子与信息学报.2009, 31(4):958-962浏览Wang Jing, Zhou Jian-jiang, and Wang Fei. 2D scattering centers extraction of objects base on GTD model[J].Journal of Electronics Information Technology.2009, 31(4):958-962[9]赵侠. SAR成像误差的分析建模与处理方法研究[D]. [博士论文],国防科学技术大学理学院,2006.[10]Zhao Xia. Error analysizing and modeling and reducing in SAR imaging[D]. [Ph.D. dissertation], Science College, National University of Defense Technology, 2006.[11]Vogel C R and Katsaggelos A G. Fast, robust total variantion -based reconstruction of noisy blurred images[J].IEEE Transaction on Image Processing.1998, 7(6):813-824

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