本文選題：合成孔徑雷達　切入點：后向投影　出處：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：合成孔徑雷達(Synthetic Aperture Radar,SAR)是一種全天時、全天候的微波成像系統(tǒng),高分辨率的特點使它在軍用和民用領(lǐng)域有著不可替代的作用。隨著合成孔徑雷達成像技術(shù)的發(fā)展,各種高分辨率成像算法應(yīng)運而生。然而高分辨率帶來的巨大計算量成為某些成像算法實際應(yīng)用的瓶頸,其中最為典型的就是后向投影(Back Projection,BP)算法。BP成像算法是一種時域成像算法,與傳統(tǒng)SAR成像算法相比,該成像算法原理簡單,并且在原理上不存在任何理論近似,能夠?qū)崿F(xiàn)高分辨率SAR成像。因此,更具有實際的研究價值�；谝陨媳尘�,本文的主要工作如下:(1)分析了BP成像算法以及快速BP(Fast Back Projection,FBP)成像算法的成像模型、實現(xiàn)原理和計算量。針對BP成像算法計算量巨大的特點,本文在BP算法的基礎(chǔ)上實現(xiàn)了一種FBP成像算法。實驗結(jié)果證明該FBP成像算法與BP成像算法成像質(zhì)量相當,并且FBP算法在一定程度上從算法層面降低了計算量。(2)研究了BP成像算法在非理想航跡下的運動誤差以及運動補償技術(shù)。分析了BP成像算法誤差的來源,建立運動誤差模型,分別提出了基于對比度最優(yōu)準則的自聚焦方法以及基于劃分子孔徑和對比度準則的BP成像自聚焦方法,實測數(shù)據(jù)成像結(jié)果驗證了這兩種運動補償算法可分別有效應(yīng)用于短孔徑和長孔徑成像處理。(3)介紹了基于計算統(tǒng)一設(shè)備架構(gòu)(Compute Unified Device Architecture,簡稱CUDA)環(huán)境下的圖形處理器(Graphic Processing Unit,GPU)編程技術(shù),分析了BP成像算法的內(nèi)在并行性,提出了一種適合GPU加速實現(xiàn)的BP成像算法加速方案;針對SAR處理數(shù)據(jù)量較大以及GPU顯存受限的問題,在此方案的基礎(chǔ)上進一步提出基于流技術(shù)的GPU優(yōu)化方案。實測數(shù)據(jù)處理結(jié)果為優(yōu)化后比優(yōu)化前平均成像速度提升約78.8%,表明該方案的有效性和可行性。
[Abstract]:Synthetic aperture radar (Synthetic Aperture, Radar, SAR) is a kind of all day long, microwave imaging system of all-weather, high resolution. It plays an irreplaceable role in military and civilian fields. With the development of synthetic aperture radar imaging technology came into being, high resolution imaging algorithm. However, large amount of calculation and high resolution the practical application has become a bottleneck of certain imaging algorithms, the most typical is the back projection (Back Projection BP) algorithm for.BP imaging algorithm is a time domain imaging algorithm, compared with the traditional SAR imaging algorithm, imaging principle of this algorithm is simple, and there is no theory in principle, can achieve high resolution SAR imaging. Therefore, the research has more practical value. Based on the above background, the main work of this paper are as follows: (1) the analysis of BP imaging algorithm and fast BP (Fast Back Projection, F BP) model imaging algorithm of imaging principle and calculation. According to BP imaging algorithm computation is huge, this paper implements a FBP imaging algorithm based on BP algorithm. The experimental results show that the FBP imaging algorithm and BP imaging algorithm for image quality, and the FBP algorithm to a certain extent from algorithm the level of the computation is decreased. (2) the motion error and motion compensation of BP imaging algorithm under the nonideal track. The sources of error of BP imaging algorithm, establish the kinematic error model are proposed based on the quasi contrast optimization autofocus method and BP imaging sub aperture and contrast criterion based on the self focusing method, real data imaging results verify that the two kinds of motion compensation algorithm can be effectively applied to respectively short aperture and long aperture imaging processing. (3) is introduced based on Compute Unified Device Architecture (Compu Te Unified Device Architecture, referred to as CUDA) graphics processor environment (Graphic Processing Unit GPU) programming technology, analyzes the inherent parallelism of BP imaging algorithm, proposes a BP imaging algorithm for GPU GPU acceleration scheme for SAR data processing; and a large amount of GPU memory constrained problem based the scheme is further proposed GPU optimization scheme based on streaming technology. The processing results of real data for optimization than before optimization average imaging speed increases about 78.8%, shows the validity and feasibility of the scheme.

【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】：2014
【分類號】：TN957.52
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本文編號：1698827