【摘要】：后向投影(back projection, BP)雷達(dá)成像算法是一種基于時(shí)域處理的雷達(dá)成像算法,具有很好的魯棒性,適用于載機(jī)任意運(yùn)動(dòng)和非均勻孔徑合成孔徑雷達(dá)成像。盡管BP算法可以不加任何近似的應(yīng)用在SAR成像中,但是它的計(jì)算效率很低,運(yùn)算量與回波脈沖數(shù)和目標(biāo)圖像像素點(diǎn)數(shù)成正比,所以在高分辨率成像應(yīng)用中運(yùn)算量非常大,運(yùn)算時(shí)間很長,所以如何提高算法運(yùn)算效率成為當(dāng)前亟待解決的問題。本文從并行化的角度出發(fā)對(duì)算法進(jìn)行優(yōu)化,在充分分析算法并發(fā)特征的基礎(chǔ)上,提出三種并行優(yōu)化方法,并設(shè)計(jì)了基于NoC多核的軟硬件成像系統(tǒng),有效提高了算法運(yùn)算效率。根據(jù)FPGA實(shí)驗(yàn)結(jié)果,本文還提出兩種系統(tǒng)實(shí)時(shí)成像方案,為該算法的實(shí)際應(yīng)用提供兩種途徑。為加快多核成像系統(tǒng)的設(shè)計(jì)效率,本文首先采用C++和SystemC建立系統(tǒng)的C模型和TLM模型,從宏觀上統(tǒng)一規(guī)劃整個(gè)系統(tǒng)架構(gòu),對(duì)系統(tǒng)軟硬件進(jìn)行了合理劃分,在早期對(duì)軟硬件進(jìn)行協(xié)同驗(yàn)證,縮短了系統(tǒng)設(shè)計(jì)周期。在系統(tǒng)模型的基礎(chǔ)上,基于NoC和多核架構(gòu),設(shè)計(jì)相應(yīng)的硬件和系統(tǒng)軟件。并將算法中運(yùn)算量最大而又具有良好并發(fā)性的反投影運(yùn)算部分封裝成硬件加速核,通過集成多個(gè)加速核,進(jìn)一步提高算法運(yùn)算效率。系統(tǒng)選用ARM處理器作為子系統(tǒng)的主控核,設(shè)計(jì)相應(yīng)的并行軟件,最終實(shí)現(xiàn)BP算法的高性能計(jì)算系統(tǒng)。在FPGA上對(duì)所設(shè)計(jì)的系統(tǒng)進(jìn)行驗(yàn)證。對(duì)于8K*4K大小的目標(biāo)圖像,在PC上不經(jīng)過加速的成像時(shí)間為5小時(shí)23分鐘,而本系統(tǒng)的成像時(shí)間只需要5分10秒,加速比高達(dá)65倍,驗(yàn)證了所設(shè)計(jì)系統(tǒng)的有效性。最后,針對(duì)8K*4K大小目標(biāo)圖像在FPGA上的實(shí)驗(yàn)結(jié)果仍然無法滿足實(shí)時(shí)成像應(yīng)用需求這一問題,本文給出分析,并提出兩種有效解決方案,為系統(tǒng)實(shí)際應(yīng)用提供了有效解決途徑。
[Abstract]:The backward projection (back projection, BP) radar imaging algorithm is a kind of radar imaging algorithm based on time domain processing. It has good robustness and is suitable for airborne arbitrary motion and non-uniform aperture synthetic aperture radar imaging. Although the BP algorithm can be used in SAR imaging without any approximation, its computational efficiency is very low. The computation quantity is in direct proportion to the number of echo pulses and the number of pixels in the target image, so it is very large in the application of high resolution imaging. The operation time is very long, so how to improve the efficiency of the algorithm becomes an urgent problem. In this paper, the algorithm is optimized from the point of view of parallelization. On the basis of fully analyzing the concurrency characteristics of the algorithm, three parallel optimization methods are proposed, and the hardware and software imaging system based on NoC multi-core is designed, which effectively improves the efficiency of the algorithm. Based on the experimental results of FPGA, two real time imaging schemes are proposed, which provide two approaches for the practical application of the algorithm. In order to accelerate the design efficiency of the multi-core imaging system, the C model and the TLM model of the system are established by C and SystemC firstly, and the whole system architecture is unified from the macro view, and the software and hardware of the system are divided reasonably. In the early stage of hardware and software co-verification, the system design cycle is shortened. Based on the system model, the corresponding hardware and software are designed based on NoC and multi-core architecture. The backprojection operation which has the largest computation and good concurrency in the algorithm is encapsulated into the hardware accelerating kernel. The efficiency of the algorithm is further improved by integrating several accelerating cores. The system chooses ARM processor as the main control core of the subsystem, designs the corresponding parallel software, and finally realizes the high performance computing system of the BP algorithm. The designed system is verified on FPGA. For the target image with the size of 8K*4K, the imaging time without acceleration on the PC is 5 hours and 23 minutes, but the imaging time of the system is only 5 minutes and 10 seconds, and the acceleration ratio is as high as 65 times. The validity of the designed system is verified. Finally, aiming at the problem that the experimental results of 8K*4K size target images on FPGA still can not meet the requirements of real-time imaging applications, this paper gives an analysis and puts forward two effective solutions, which provide an effective solution for the practical application of the system.
【學(xué)位授予單位】：南京大學(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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