基于直接采樣法和子空間優(yōu)化法的多介質目標混合逆散射成像方法

周輝林; 鄭靈輝; 莫仲念; 王玉皞; 陳良兵

doi:10.11999/JEIT160534

基于直接采樣法和子空間優(yōu)化法的多介質目標混合逆散射成像方法

doi: 10.11999/JEIT160534 cstr: 32379.14.JEIT160534

基金項目:

國家自然科學基金(61561034, 61261010, 41505015)，江西省自然科學基金(2015BAB207001)，江西省科技支撐計劃(20151BBE50090)，江西省研究生創(chuàng)新專項基金(YC2016-S068)

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- 文章訪問數(shù): 1658
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-05-26
- 修回日期: 2016-10-11
- 刊出日期: 2017-03-19

DSM-SOM Based Hybrid Inverse Scattering Method for Multiple Dielectric Objects Reconstruction

Funds:

The National Natural Science Foundation of China (61561034, 61261010, 41505015), Jiangxi Provincial Natural Science Foundation (2015BAB207001), The Projects in the Jiangxi Provincial Science Technology Pillar Program (20151BBE- 50090), Jiangxi Provincial Graduate Innovation Special Foundation (YC2016-S068)

摘要

摘要: 該文提出一種結合定性與定量成像方法優(yōu)勢的混合電磁場逆散射成像方法，并將該方法應用于重構多介質目標的電性能參數(shù)的空間分布信息。該混合成像方法首先利用基于直接采樣法(Direct Sampling Method, DSM)的定性方法快速重構目標的感興趣區(qū)域(Region Of Interesting, ROI)、目標形狀及目標個數(shù)的先驗信息。在此基礎上,利用基于子空間優(yōu)化定量方法結合該先驗信息迭代修正目標的幾何形狀信息，并重構目標的電性能參數(shù)的空間分布?；诜颇鶢枌嶒炇覍崪y散射場數(shù)據(jù)表示，該方法與子空間優(yōu)化法SOM(Subspace-based Optimization Method)定量成像精度相比擬的情況下，極大地降低了定量方法的計算復雜度和提高算法收斂速度。
- 逆散射 /
- 直接采樣方法 /
- 子空間優(yōu)化方法
Abstract: This paper proposes a hybrid electromagnetic field inverse scattering imaging method based on the advantages of the qualitative and quantitative imaging methods，and it is applied to rebuilding the space distribution information of electric parameters for multi objects. First, the prior knowledge of the Region Of Interesting (ROI) of target, object shape and target number is reconstructed by using Direct Sampling Method (DSM). Then, the geometry information of the objects and the space iteratively corrected distribution information of electric parameters is reconstructed by Subspace-based Optimization quantitative Method(SOM). The experimental result for the scattering field data of Fresnel laboratory shows that the imaging accuracy of this method is comparable to SOM. More over, the proposed technique greatly reduces the computational complexity and improves the convergence speed.
- Inverse scattering /
- Direct Sampling Methods (DSM) /
- Subspace-based Optimization quantitative Method (SOM)

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參考文獻(14)

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