基于壓縮感知的稀疏采樣與成像方法研究
發(fā)布時(shí)間：2018-07-28 18:06
【摘要】：作為一種信息獲取與處理的新理論,壓縮感知已成為當(dāng)前信號(hào)處理領(lǐng)域的一個(gè)熱門研究方向。壓縮感知理論指出:在滿足“信號(hào)可壓縮”與“觀測(cè)系統(tǒng)與表示系統(tǒng)非相關(guān)”這兩個(gè)條件下,能夠從信號(hào)的少量采樣數(shù)據(jù)中高概率復(fù)原信號(hào),使得信號(hào)在空、時(shí)、譜等上的超分辨成為可能。由于大部分無線電信號(hào)具有可壓縮性,即在某個(gè)正交/過完備字典下的編碼系數(shù)是稀疏的,因此壓縮感知在無線通信和成像等諸多應(yīng)用中有著廣泛的應(yīng)用前景。例如在合成孔徑雷達(dá)成像中,雷達(dá)接收到的回波可以看作是多個(gè)強(qiáng)散射中心回波的疊加,這種稀疏性先驗(yàn)使得基于壓縮感知理論的稀疏成像成為可能。目前,雖然壓縮感知在雷達(dá)成像顯示出初步的成功,但是仍存在幾個(gè)問題:第一,目前已有的壓縮感知雷達(dá)成像方法基于壓縮感知理論,僅利用了目標(biāo)的稀疏性先驗(yàn)進(jìn)行較少方位向脈沖下的超分辨成像。然而,隨著方位向脈沖數(shù)的減少,成像質(zhì)量隨之迅速下降。第二,由于距離維的采樣容易降低目標(biāo)能量,目前已有的壓縮感知雷達(dá)成像大部分均在方位維進(jìn)行采樣。然而,隨著寬帶/超寬帶微波成像在安全檢測(cè)與非破壞性控制等領(lǐng)域中的迫切需求,距離-方位維聯(lián)合的超分辨技術(shù)已成為亟待解決的研究難題。針對(duì)上述問題,本文研究了基于壓縮感知的稀疏采樣與成像方法。具體工作如下:(1)設(shè)計(jì)了一種基于壓縮感知的距離-方位聯(lián)合稀疏雷達(dá)成像方法。首先對(duì)合成孔徑雷達(dá)回波信號(hào)的稀疏性進(jìn)行分析,研究了稀疏基的構(gòu)造,實(shí)現(xiàn)快時(shí)間和慢時(shí)間兩個(gè)維度上的聯(lián)合欠采樣。將該方法分別用于SAR和ISAR的超分辨成像,實(shí)驗(yàn)結(jié)果表明:壓縮感知成像方法相對(duì)于傳統(tǒng)微波成像方法,可以在低脈沖數(shù)下可以獲得更低的旁瓣和更高的成像質(zhì)量。(2)設(shè)計(jì)了一種基于顯著性先驗(yàn)和加權(quán)L1優(yōu)化的稀疏成像方法。除了目標(biāo)的稀疏性先驗(yàn)之外,目標(biāo)的顯著性與幾何結(jié)構(gòu)可以作為先驗(yàn)信息以改善較少采樣下的成像質(zhì)量。首先利用低分辨成像結(jié)果提取視覺顯著圖,從中區(qū)分出顯著目標(biāo)區(qū)域。其次,在重構(gòu)過程中對(duì)目標(biāo)和背景加以不同的權(quán)值,來達(dá)到抑制背景中的雜波,同時(shí)增強(qiáng)目標(biāo)區(qū)域中的強(qiáng)散射點(diǎn)的目標(biāo)。將該方法用于256個(gè)方位維脈沖數(shù)的Yak-42數(shù)據(jù)的超分辨成像,實(shí)驗(yàn)結(jié)果表明:基于顯著性先驗(yàn)的加權(quán)L1優(yōu)化可以區(qū)別地對(duì)待目標(biāo)和背景,實(shí)現(xiàn)成像時(shí)增強(qiáng)目標(biāo)散射點(diǎn)同時(shí)抑制背景雜波。(3)設(shè)計(jì)了一種基于圖Laplacian正則的合作式稀疏成像方法。除了目標(biāo)的稀疏性先驗(yàn)和目標(biāo)顯著性之外,目標(biāo)鄰近距離單元的相關(guān)性也可以進(jìn)一步改善成像質(zhì)量。在基于顯著圖的加權(quán)L1優(yōu)化成像基礎(chǔ)上,挖掘臨近距離單元的相似性,構(gòu)成圖Laplacian正則項(xiàng),對(duì)原本的稀疏優(yōu)化問題增加結(jié)構(gòu)性約束。針對(duì)該優(yōu)化問題,設(shè)計(jì)了一種基于增廣拉格朗日乘子法的交替優(yōu)化求解算法。將該方法用于點(diǎn)信號(hào)的256個(gè)方位維脈沖數(shù)的Yak-42超分辨成像,實(shí)驗(yàn)結(jié)果表明:圖Laplacian正則項(xiàng)有效地降低了背景雜波中孤立的散射點(diǎn),而目標(biāo)上的強(qiáng)散射之點(diǎn)之間由于位于鄰近距離單元,受結(jié)構(gòu)性約束的影響很小。(4)設(shè)計(jì)了一種模擬信號(hào)稀疏采樣的模擬信息轉(zhuǎn)換器(Analog-to-Information Converter,AIC)實(shí)現(xiàn)。研究了基于壓縮感知的模擬信號(hào)采樣,設(shè)計(jì)一種基于MWC結(jié)構(gòu)的硬件仿真平臺(tái),針對(duì)無線電通信系統(tǒng)中存在的多寬帶信號(hào),分析了MWC系統(tǒng)的結(jié)構(gòu)、原理,實(shí)驗(yàn)驗(yàn)證其重構(gòu)效果和穩(wěn)定性,為稀疏雷達(dá)成像中的距離維稀疏采樣的硬件實(shí)現(xiàn)奠定基礎(chǔ)。
[Abstract]:As a new theory of information acquisition and processing, compressed sensing has become a hot research direction in the field of signal processing. The theory of compressed sensing indicates that, under the two conditions of satisfying "signal compressibility" and "non correlation of observation system and representation system", it is possible to recover signals from a small amount of sampled data from the signal, Because most of the radio signals are compressible, that is, the coding coefficients in a certain orthogonal / overcomplete dictionary are sparse, so compressed sensing has a wide application prospect in many applications such as wireless communication and imaging. For example, in synthetic aperture radar imaging, thunder The received echo can be regarded as the superposition of multiple strong scattering center echoes. This sparsity prior makes the sparse imaging based on compressed sensing theory possible. At present, although compression perception has shown a preliminary success in radar imaging, there are still several problems: first, the existing compression sensing radar imaging method is present. Based on the theory of compressed sensing, only using the sparsity of the target to carry out the super-resolution imaging with less azimuth to the pulse. However, with the reduction of the number of azimuth pulses, the imaging quality drops rapidly. Second, since the sampling of the distance dimension is easy to reduce the target energy, most of the existing compressed sensing radar imaging are in the azimuth. However, with the urgent need of broadband / ultra wideband microwave imaging in the fields of security detection and non destructive control, the range azimuth combination superresolution technology has become a difficult problem to be solved. In this paper, this paper studies the sparse sampling and imaging method based on the compression sensitivity. The specific work is as follows (1) a range azimuth joint sparse radar imaging method based on compressed sensing is designed. Firstly, the sparsity of the SAR echo signal is analyzed, the construction of the sparse base is studied, and the joint undersampling on two dimensions of fast and slow time is realized. The method is applied to the super-resolution imaging of SAR and ISAR respectively. The results show that the compression sensing imaging method can obtain lower sidelobe and higher imaging quality under the low pulse number compared with the traditional microwave imaging method. (2) a sparse imaging method based on significant prior and weighted L1 optimization is designed. Besides the sparsity prior of the target, the significance and geometric structure of the target can be obtained. As a priori information, the image quality under less sampling is improved. Firstly, the visual significant image is extracted from the result of low resolution imaging, and the significant target area is separated from the middle area. Secondly, the target and the background are different weights in the reconstruction process to suppress the clutter in the background and enhance the target of the strong scattering point in the target area. This method is applied to the super-resolution imaging of Yak-42 data of 256 azimuth pulse numbers. The experimental results show that the weighted L1 optimization based on significant prior can treat the target and the background discriminately, and the target scattering points are enhanced and the background clutter can be suppressed simultaneously. (3) a cooperative sparse imaging based on the graph Laplacian regularization is designed. Methods. In addition to the sparse prior and target significance of the target, the correlation of the target proximity unit can further improve the imaging quality. On the basis of the weighted L1 optimization imaging based on the saliency graph, the similarity of the adjacent distance units is excavated, and the Laplacian regular term is formed, which increases the structure of the original sparse optimization problem. In order to solve this problem, an alternating optimization algorithm based on the augmented Lagrange multiplier method is designed. This method is applied to the Yak-42 super-resolution imaging of 256 azimuth pulse numbers of point signals. The experimental results show that the figure Laplacian canonical term effectively reduces the isolated scattering points in the background clutter and the strong scattering on the target. Due to the proximity unit, the influence of structural constraints is very small. (4) a simulated signal sparse sampling analog information converter (Analog-to-Information Converter, AIC) is designed. The analog signal sampling based on compressed sensing is studied, and a hardware simulation platform based on MWC structure is designed for wireless communication. The multi wideband signal in the electrical communication system is analyzed. The structure and principle of the MWC system are analyzed. The effect and stability of the reconfiguration are verified by experiments. It lays the foundation for the hardware realization of the sparse radar imaging in the sparse sampling.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52
[Abstract]:As a new theory of information acquisition and processing, compressed sensing has become a hot research direction in the field of signal processing. The theory of compressed sensing indicates that, under the two conditions of satisfying "signal compressibility" and "non correlation of observation system and representation system", it is possible to recover signals from a small amount of sampled data from the signal, Because most of the radio signals are compressible, that is, the coding coefficients in a certain orthogonal / overcomplete dictionary are sparse, so compressed sensing has a wide application prospect in many applications such as wireless communication and imaging. For example, in synthetic aperture radar imaging, thunder The received echo can be regarded as the superposition of multiple strong scattering center echoes. This sparsity prior makes the sparse imaging based on compressed sensing theory possible. At present, although compression perception has shown a preliminary success in radar imaging, there are still several problems: first, the existing compression sensing radar imaging method is present. Based on the theory of compressed sensing, only using the sparsity of the target to carry out the super-resolution imaging with less azimuth to the pulse. However, with the reduction of the number of azimuth pulses, the imaging quality drops rapidly. Second, since the sampling of the distance dimension is easy to reduce the target energy, most of the existing compressed sensing radar imaging are in the azimuth. However, with the urgent need of broadband / ultra wideband microwave imaging in the fields of security detection and non destructive control, the range azimuth combination superresolution technology has become a difficult problem to be solved. In this paper, this paper studies the sparse sampling and imaging method based on the compression sensitivity. The specific work is as follows (1) a range azimuth joint sparse radar imaging method based on compressed sensing is designed. Firstly, the sparsity of the SAR echo signal is analyzed, the construction of the sparse base is studied, and the joint undersampling on two dimensions of fast and slow time is realized. The method is applied to the super-resolution imaging of SAR and ISAR respectively. The results show that the compression sensing imaging method can obtain lower sidelobe and higher imaging quality under the low pulse number compared with the traditional microwave imaging method. (2) a sparse imaging method based on significant prior and weighted L1 optimization is designed. Besides the sparsity prior of the target, the significance and geometric structure of the target can be obtained. As a priori information, the image quality under less sampling is improved. Firstly, the visual significant image is extracted from the result of low resolution imaging, and the significant target area is separated from the middle area. Secondly, the target and the background are different weights in the reconstruction process to suppress the clutter in the background and enhance the target of the strong scattering point in the target area. This method is applied to the super-resolution imaging of Yak-42 data of 256 azimuth pulse numbers. The experimental results show that the weighted L1 optimization based on significant prior can treat the target and the background discriminately, and the target scattering points are enhanced and the background clutter can be suppressed simultaneously. (3) a cooperative sparse imaging based on the graph Laplacian regularization is designed. Methods. In addition to the sparse prior and target significance of the target, the correlation of the target proximity unit can further improve the imaging quality. On the basis of the weighted L1 optimization imaging based on the saliency graph, the similarity of the adjacent distance units is excavated, and the Laplacian regular term is formed, which increases the structure of the original sparse optimization problem. In order to solve this problem, an alternating optimization algorithm based on the augmented Lagrange multiplier method is designed. This method is applied to the Yak-42 super-resolution imaging of 256 azimuth pulse numbers of point signals. The experimental results show that the figure Laplacian canonical term effectively reduces the isolated scattering points in the background clutter and the strong scattering on the target. Due to the proximity unit, the influence of structural constraints is very small. (4) a simulated signal sparse sampling analog information converter (Analog-to-Information Converter, AIC) is designed. The analog signal sampling based on compressed sensing is studied, and a hardware simulation platform based on MWC structure is designed for wireless communication. The multi wideband signal in the electrical communication system is analyzed. The structure and principle of the MWC system are analyzed. The effect and stability of the reconfiguration are verified by experiments. It lays the foundation for the hardware realization of the sparse radar imaging in the sparse sampling.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52
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