本文選題：合成孔徑雷達(dá) + 圖形處理器��； 參考：《南京航空航天大學(xué)》2017年碩士論文

【摘要】：機載合成孔徑雷達(dá)(Synthetic Aperture Radar,簡稱SAR)能夠全天時、全天候地工作,并且具有高分辨率、廣視域等眾多優(yōu)點,在多個領(lǐng)域得到廣泛的應(yīng)用。波數(shù)域算法是一種高精度的SAR處理算法。補償回波的相位誤差是保證成像質(zhì)量的必要手段,因為相位誤差具有二維空變性,因此需要對傳統(tǒng)的自聚焦技術(shù)進(jìn)行改進(jìn),以抑制相位誤差的二維空變性。高速發(fā)展的圖形處理器(Graphics Processing Unit,簡稱GPU)和統(tǒng)一計算架構(gòu)(Compute Unified Device Architecture,簡稱CUDA)技術(shù)的發(fā)展,使得GPU的通用并行計算得到普及,成為了SAR成像算法的一種新型運算平臺。基于以上所述,本文主要工作包括:(1)分析SAR成像模型,波數(shù)域成像算法原理,以及基于慣導(dǎo)測量數(shù)據(jù)的運動補償原理,并分析算法的并行性,設(shè)計該算法基于GPU平臺的并行方案。點目標(biāo)仿真數(shù)據(jù)處理結(jié)果顯示該算法在GPU平臺上可獲得30倍左右的加速比,驗證了并行方案的可行性。(2)針對條帶SAR相位誤差的方位向空變性,討論了一種子孔徑自聚焦算法——PGA-MD算法。該算法通過PGA算法計算子孔徑的精確的相位誤差估計,并利用MD算法校正誤差估計中的線性相位,最后將子孔徑相位誤差拼接得到全孔徑相位誤差。并且分析PGA-MD算法的并行性,設(shè)計并行方案。最后通過實測數(shù)據(jù)處理,驗證了PGA-MD算法的有效性以及并行方案的可行性。(3)針對相位誤差的距離向空變性,提出改進(jìn)的距離向分塊自聚焦處理技術(shù)。該算法在距離向進(jìn)行部分重疊的分塊,并對每個距離塊進(jìn)行PGA-MD相位誤差估計和補償以及子圖像自聚焦處理,得到每個距離塊的成像結(jié)果。并且本文提出了子圖像互相關(guān)配準(zhǔn)算法來實現(xiàn)相鄰距離塊的配準(zhǔn),并用加權(quán)拼接算法對配準(zhǔn)后的圖像進(jìn)行拼接,得到完整的成像結(jié)果。實測數(shù)據(jù)處理結(jié)果表明,距離向分塊自聚焦算法能夠進(jìn)一步提高聚焦效果。
[Abstract]:Airborne synthetic Aperture Radar (SAR) can work all day, all weather, and has many advantages such as high resolution, wide view and so on, so it has been widely used in many fields. Wavenumber domain algorithm is a high precision SAR processing algorithm. Compensating the phase error of echo is a necessary means to guarantee the imaging quality, because the phase error has two-dimensional space variability, so it is necessary to improve the traditional self-focusing technique to suppress the two-dimensional null variation of phase error. With the development of Graphics processing Unit (GPU) and Unified Compute Unified device Architecture (CUDAE), GPU has become a new computing platform for SAR imaging. Based on the above, the main work of this paper is to analyze SAR imaging model, wavenumber domain imaging algorithm and motion compensation principle based on inertial navigation measurement data, and analyze the parallelism of the algorithm. The parallel scheme based on GPU platform is designed. The result of point target simulation data processing shows that the algorithm can get about 30 times speedup on GPU platform, which verifies the feasibility of the parallel scheme. A subaperture autofocus algorithm, PGA-MD algorithm, is discussed. The PGA algorithm is used to calculate the accurate phase error estimation of the sub-aperture and the MD algorithm is used to correct the linear phase in the error estimation. Finally, the sub-aperture phase error is spliced to obtain the full aperture phase error. The parallelism of PGA-MD algorithm is analyzed and the parallel scheme is designed. Finally, the validity of the PGA-MD algorithm and the feasibility of the parallel scheme are verified by the experimental data processing. (3) in view of the range spatial variability of the phase error, an improved range block autofocus processing technique is proposed. The algorithm partially overlaps blocks in the range direction and performs PGA-MD phase error estimation and compensation for each distance block as well as sub-image self-focusing processing to obtain the imaging results of each range block. In this paper, a sub-image cross-correlation registration algorithm is proposed to realize the registration of adjacent distance blocks, and the image is stitched by weighted stitching algorithm, and the complete imaging results are obtained. The experimental data processing results show that the range block autofocus algorithm can further improve the focusing effect.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.52

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本文編號：2020266