發(fā)布時間：2018-02-15 08:14

  本文關(guān)鍵詞： 線性算法 非線性算法 一階Born近似 格林函數(shù) 二階Born近似 子空間優(yōu)化　出處：《南昌大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著時代的發(fā)展,微波成像技術(shù)的運用領(lǐng)域也越來越多,其在日常生活和軍事領(lǐng)域的應(yīng)用所占的比例越來越重,傳統(tǒng)的微波成像算法已經(jīng)不能滿足人們?nèi)粘Ｐ枨�。因�?開展與微波成像相關(guān)的研究不管是對信息時代的國防還是逐步高度發(fā)展的社會都具有重要意義。對微波成像研究是世界前沿性課題,其主要功能是對目標(biāo)成像,因此如何提高微波成像效果和求解目標(biāo)的電性參數(shù)是研究微波成像技術(shù)的主要研究內(nèi)容之一。本文主要研究了二階Born近似和非線性算法子空間優(yōu)化方法,并通過實驗分析對比二階Born近似和子空間優(yōu)化方法的優(yōu)劣性。針對二階Born近似,本文基于一階Born近似推導(dǎo)出二階Born近似,并將其線性化,然后利用不精確牛頓算法對目標(biāo)成像�？紤]如何優(yōu)化迭代算法,減少計算代價,并在多發(fā)多收的情況下,考慮如何將多個發(fā)射天線時的成像效果進行揉合。針對子空間優(yōu)化方法,本文利用修正后的目標(biāo)函數(shù)對目標(biāo)進行成像,格林函數(shù)采用零階二類漢克爾函數(shù),并通過相關(guān)理論推導(dǎo)出子空間優(yōu)化相關(guān)參數(shù)的表達式,通過選取適當(dāng)截斷點,計算出確定部分感應(yīng)電流,再通過迭代優(yōu)化得出模糊部分感應(yīng)電流,最后確定目標(biāo)網(wǎng)格內(nèi)的總電場,反演出目標(biāo)的相對介電常數(shù),從而對目標(biāo)成像。本文實驗數(shù)據(jù)來源于法國馬賽市菲涅爾研究所。實驗表明,在利用二階Born近似對單目標(biāo)成像時,其結(jié)果較好的反映出目標(biāo)的位置和大小,而對于目標(biāo)的介電常數(shù),在反演結(jié)果中并不能較好的表現(xiàn)出來,另外,對多目標(biāo)成像時,其結(jié)果較差。而當(dāng)利用非線性電磁逆成像算法子空間優(yōu)化對目標(biāo)成像時,不僅可以較為準(zhǔn)確的反應(yīng)目標(biāo)的位置大小,同時也能較好的計算出目標(biāo)的介電常數(shù)。另外,子空間優(yōu)化方法在對多目標(biāo)成像時,也能較好的反映出目標(biāo)的各種幾何參數(shù)和介電常數(shù),但計算代價較大。因此,非線性算法子空間優(yōu)化方法在對目標(biāo)成像時表現(xiàn)出更高的準(zhǔn)確性。
[Abstract]:With the development of the times, the application of microwave imaging technology is more and more, its application in daily life and military field is more and more heavy, the traditional microwave imaging algorithm can no longer meet the daily needs of people. The research related to microwave imaging is of great significance not only to national defense in the information age, but also to a society with a high degree of development. Microwave imaging is a leading subject in the world, and its main function is the imaging of targets. Therefore, how to improve the effect of microwave imaging and solve the electrical parameters of the target is one of the main research contents of microwave imaging technology. In this paper, we mainly study the second-order Born approximation and nonlinear algorithm subspace optimization method. The advantages and disadvantages of the second-order Born approximation and the subspace optimization method are compared by experiments. For the second-order Born approximation, the second-order Born approximation is derived based on the first-order Born approximation and linearized. Then we use the inexact Newton algorithm to image the target. Consider how to optimize the iterative algorithm, reduce the computational cost, and in the case of multiple collection, Considering how to combine the imaging effects of multiple transmit antennas, the modified objective function is used to image the target, and the Green function uses the zero-order Hankel function for subspace optimization. The expression of subspace optimization parameters is deduced by correlation theory. By selecting the appropriate truncation point, the partial inductive current is calculated and the fuzzy partial inductive current is obtained by iterative optimization. Finally, the total electric field in the target grid and the relative dielectric constant of the target are determined. The experimental data are obtained from the Fresnel Institute in Marseille, France. The experimental results show that the second order Born approximation is used to image a single target. The results reflect the position and size of the target well, but the dielectric constant of the target is not well represented in the inversion results. The results are not good. When the nonlinear electromagnetic inverse imaging algorithm is used to optimize the imaging of the target in subspace, not only the position of the target can be accurately reflected, but also the dielectric constant of the target can be calculated. The subspace optimization method can also reflect all kinds of geometric parameters and dielectric constant of the target, but the calculation cost is high. The subspace optimization method of nonlinear algorithm is more accurate in imaging target.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN015;TP391.41

【共引文獻】

相關(guān)博士學(xué)位論文 前1條

1 申建華;基于TSOM算法的電磁波逆散射成像研究[D];浙江大學(xué);2013年

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本文編號：1512831