本文選題：超寬帶合成孔徑雷達(dá)　切入點(diǎn)：圖形處理器　出處：《國(guó)防科學(xué)技術(shù)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：本文針對(duì)常規(guī)后向投影(Back Projection,BP)算法運(yùn)算量大的問(wèn)題,綜合硬件多核并行以及孔徑分級(jí)優(yōu)化兩類技術(shù),實(shí)現(xiàn)對(duì)高分辨率超寬帶合成孔徑雷達(dá)(Ultra Wideband Synthetic Aperture Radar,UWB-SAR)實(shí)時(shí)成像,具有重要的理論意義和工程實(shí)用價(jià)值,論文主要工作如下:建立了步進(jìn)頻率信號(hào)條帶式SAR的回波模型,分析了BP算法距離壓縮和方位壓縮過(guò)程。詳細(xì)介紹了圖形處理器(Graphic Processing Unit,GPU)和統(tǒng)一計(jì)算設(shè)備架構(gòu)(Compute Unified Device Architecture,CUDA)編程知識(shí),得出多核并行化、存儲(chǔ)資源分配及訪問(wèn)影響最終算法效率的基本結(jié)論,是后續(xù)并行化方法優(yōu)化的基礎(chǔ)。提出了一種基于GPU的并行優(yōu)化網(wǎng)格BP算法。重點(diǎn)對(duì)方位壓縮過(guò)程設(shè)計(jì)了三種優(yōu)化方法,利用網(wǎng)格BP算法結(jié)構(gòu)減少了多線程訪問(wèn)原始回波數(shù)據(jù)的次數(shù);利用紋理存儲(chǔ)器加速了插值計(jì)算效率;合理設(shè)計(jì)了子圖像存儲(chǔ)方式,避免共享存儲(chǔ)器訪問(wèn)沖突。從而有效利用GPU的多核架構(gòu)提高了成像效率。提出了一種基于GPU的并行化因式分解快速BP(Factorized Fast BP,FFBP)算法。設(shè)計(jì)了FFBP算法的實(shí)現(xiàn)流程,分析了FFBP在算法層面的加速比,最后利用GPU實(shí)現(xiàn)了并行加速。與基于GPU的BP算法相比,成像效率獲得進(jìn)一步顯著提升。為了有效處理實(shí)測(cè)UWB-SAR回波數(shù)據(jù),設(shè)計(jì)了上述兩種算法的分塊實(shí)現(xiàn)途徑,解決輸入數(shù)據(jù)量大但GPU存儲(chǔ)資源不足的問(wèn)題,獲得了聚焦良好的高分辨率圖像,與CPU單線程實(shí)現(xiàn)的BP算法相比,加速比分別為70倍和232倍左右,驗(yàn)證了論文所提方法的有效性。
[Abstract]:In this paper, aiming at the problem of large computation of conventional back projection back projection (BP) algorithm, combining hardware multi-core parallelism and aperture grading optimization techniques, the real-time imaging of Ultra Wideband Synthetic Aperture Radarar (UWB-SAR) for high resolution ultra-wideband synthetic aperture radar (UWB-SAR) is realized. It has important theoretical significance and practical engineering value. The main work of this paper is as follows: the echo model of striped SAR with step frequency signal is established. The process of BP algorithm distance compression and azimuth compression is analyzed, and the programming knowledge of graphic processor graphic Processing Unit (GPU) and Unified Computing equipment Architecture (Compute Unified Device Architecture) are introduced in detail, and the multi-core parallelization is obtained. The basic conclusion that storage resource allocation and access affect the efficiency of the final algorithm is the basis of the optimization of the subsequent parallelization method. A parallel optimization mesh BP algorithm based on GPU is proposed, and three optimization methods are designed for the azimuth compression process. The mesh BP algorithm structure is used to reduce the number of multithreading access to the original echo data; the texture memory is used to accelerate the interpolation calculation efficiency; the sub-image storage mode is designed reasonably. In order to avoid the access conflict of shared memory, the multi-core architecture of GPU is used effectively to improve the imaging efficiency. A parallel factorization algorithm based on GPU is proposed for fast BP(Factorized Fast BP- FFBPs, and the implementation flow of FFBP algorithm is designed. The speedup ratio of FFBP at the algorithm level is analyzed. At last, parallel acceleration is realized by using GPU. Compared with BP algorithm based on GPU, the imaging efficiency is further improved. In order to solve the problem that the input data is large but the GPU storage resource is insufficient, the high resolution image with good focus is obtained, which is compared with the BP algorithm implemented by CPU single thread. The speedup ratio is about 70 times and 232 times respectively, which verifies the effectiveness of the proposed method.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)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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