本文選題：SAR成像 + GPU�。� 參考：《南京理工大學》2017年碩士論文

【摘要】：合成孔徑雷達(SAR)不受時間、天氣、地域等因素的影響,可以全天候工作,作用距離遠,廣泛應用于目標探測、識別和定位,戰(zhàn)場偵察監(jiān)視,自然災害預報,資源勘探等軍事和民用領域。SAR可以獲得高分辨圖像,但是成像算法復雜,需要處理大量的回波數(shù)據(jù),運算量非常大,對硬件平臺提出了很高的要求。本文針對星載SAR在軌成像處理的應用需求,開展了 SAR快速成像、目標檢測與成像評估方法的研究,主要內容包括:(1)分析了星載SAR信號處理系統(tǒng)的設計因素,給出了一種以嵌入式GPU為核心的星載SAR信號處理系統(tǒng),分析了 GPU并行處理效率,探討了 CUDA平臺的編程模型及存儲模型。(2)詳細討論了 SAR回波模型,設計了 SAR回波信號存儲方式;針對Chirp Scaling(CS)成像算法進行了并行處理的結構優(yōu)化;針對NVIDIA GPU信號處理平臺,設計了 CS成像算法并行程序;實驗結果表明,相對于CPU平臺,該系統(tǒng)取得了幾十甚至幾百倍的加速比。(3)針對雙參數(shù)CFAR算法進行了相應的并行處理結構優(yōu)化,設計了雙參數(shù)CFAR算法的并行程序。利用真實的艦船SAR圖像進行了驗證,實驗結果表明該方法目標檢測準確,與CPU平臺進行對比,加速比達到了 400以上。(4)詳細討論了 SAR圖像質量客觀評價指標,利用C#設計了 SAR圖像質量客觀評價的GUI測試軟件,并對本文給出的星載SAR快速成像平臺、傳統(tǒng)CPU成像平臺所成SAR圖像質量進行了對比分析。
[Abstract]:The synthetic Aperture Radar (SAR), which is not affected by time, weather, region and other factors, can work around the clock and has a long range of functions. It is widely used in target detection, identification and positioning, battlefield reconnaissance and surveillance, and natural disaster prediction. High resolution images can be obtained from SAR in military and civil fields such as resource exploration, but the imaging algorithms are complex and need to deal with a large amount of echo data. In order to meet the requirements of spaceborne SAR imaging processing in orbit, this paper studies the methods of SAR fast imaging, target detection and imaging evaluation. The main content of this paper is to analyze the design factors of spaceborne SAR signal processing system. A space-borne SAR signal processing system based on embedded GPU is presented. The efficiency of GPU parallel processing is analyzed. The programming model and storage model of CUDA platform are discussed. The SAR echo model is discussed in detail. The storage mode of SAR echo signal is designed, the structure of parallel processing is optimized for Chirp scaling CSC imaging algorithm, the parallel program of CS imaging algorithm is designed for NVIDIA GPU signal processing platform, and the experimental results show that, compared with CPU platform, a parallel program for CS imaging algorithm is designed. The system achieves a speedup ratio of several tens or even hundreds of times. The parallel processing structure of the two-parameter CFAR algorithm is optimized and the parallel program of the two-parameter CFAR algorithm is designed. The real ship SAR image is used to verify the method. The experimental results show that the method is accurate and compared with CPU platform. The speedup ratio is more than 400. 4) the objective evaluation index of SAR image quality is discussed in detail. The GUI test software for objective evaluation of SAR image quality is designed by using C #, and the SAR image quality of Spaceborne SAR fast imaging platform and traditional CPU imaging platform is compared and analyzed in this paper.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.52

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本文編號：1790441