合成孔徑雷達是一種常被應用在執行遙測任務的雷達系統，而此雷達系統藉由雷達天線的移動來合成一個大孔徑的真實天線，藉此得到更好的影像解析度。為了追求更好的解析度，本論文主要研究合成孔徑雷達常用的成像演算法與都普勒質心估測，在成像演算法方面，我們比較了不同的二次距離方向壓縮與所提出的距離區塊獨立之二次距離壓縮法對成像演算法的效能影響，而我們會以距離都普勒成像演算法配上不同的二次距離方向壓縮法來觀察成像結果，同時，估計每個二次距離方向壓縮方法的計算量，另外，我們也會介紹鳥鳴刻度成像演算法的步驟，並且與距離都普勒成像演算法比較其成像結果，而在成像步驟的距離偏移修正中，距離都普勒成像演算法主要以內插器逐點修正距離與方位方向的取樣點，而鳥鳴刻度成像演算法會以相位乘法一次修正擁有同樣都普勒頻率的目標物之距離偏移，但是所需的相位乘法參數需隨時而變化，因此，需要評估並比較距離都普勒成像演算法與鳥鳴刻度成像演算法在計算複雜度與效能上的差異。而在都普勒質心估測演算法方面，主要觀察三個都普勒質心估測演算法在不同場景與不同訊雜比下，估測出的都普勒質心之正確性，並以分析回波之特徵，來判定該次的都普勒質心估測是否為可信任的結果。

Synthetic Aperture Radar (SAR) is a radar system that is often used to perform telemetry tasks. This radar system synthesizes a large aperture to improve image resolution by moving the radar antenna. In order to pursue better resolution, this paper mainly implements the imaging algorithms and Doppler centroid estimation commonly used. In terms of imaging algorithm, we compare different secondary range compression method and proposed compression method. We will use the range Doppler algorithm with different secondary range compression method to compare the imaging performance and the computational complexity with different secondary range compression method. In addition, we will also introduce the chirp scaling algorithm, and compare the imaging performance with the range Doppler algorithm. In the range cell migration correction, the range Doppler algorithm mainly uses the interpolator to correct the position of sampling point in the range and azimuth direction point by point, while the chirp scaling algorithm correct the position of sampling points with the same Doppler frequency by phase multiplication. The parameters of phase multiplication change with time, therefore, we have to evaluate the imaging performance and the computational complexity when we select the imaging algorithm. In the part of the Doppler centroid estimation algorithm, first of all, we will check the correctness of the Doppler centroid estimation in different scenarios and in different signal-to-noise, and then, we can determine whether the Doppler centroid estimation results can be used for imaging algorithms by analyzing the characteristics of the received signal.

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