臺灣博碩士論文加值系統

在逆合成孔徑雷達的成像中，目標物非預期的運動狀況會對成像結果造成影響，產生出模糊且無法辨識的影像，必須適當的評估目標物移動中的各項參數變化，去除會影響結果的因素，稱之為運動補償。本論文將模擬目標物運動下的電磁波回波訊號，建立逆合成孔徑雷達影像成像演算法，以逆合成孔徑雷達影像的熵作為影像品質的評估標準，並使用熵最小化法預估目標物的運動情形，利用粒子群優化法對其進行優化，得到經過運動補償的ISAR影像。

In the imaging of inverse synthetic aperture radar, the unexpectedly motion of the target can affect the imaging results, producing a blurry and unrecognizable images. We need to accurate evaluation the parameters in target moving, remove the factors that will affect the results. The procedure is called motion compensation. The electromagnetic wave echo signal under the target moving is simulated, and the inverse synthetic aperture radar image imaging algorithm is designed in this thesis. The Entropy of inverse synthetic aperture radar image is used as the evaluation standard of image quality, and Entropy minimization method is used to estimate the movement of the target. Finally, Particle swarm optimization is utilized to optimize it and get the motion compensated inverse synthetic aperture radar image.

摘要 i
ABSTRACT ii
目錄 iii
表目錄 iv
圖目錄 v
第一章 緒論 1
1.1 研究背景介紹 1
1.2 研究動機與目的 2
1.3 論文內容大綱 3
第二章 相關文獻回顧 4
2.1雷達截面積(Radar Cross Section, RCS) 4
2.2都卜勒效應(Doppler effect) 4
2.3合成孔徑雷達(Synthetic Aperture Radar , SAR) 5
2.4逆合成孔徑雷達(Inverse Synthetic Aperture Radar , ISAR) 7
2.5模擬運動補償方法 12
2.6互相關方法(Cross-Correlation method) 14
2.7熵最小化方法(Entropy Minimization method) 15
2.8粒子群最佳化法(Particle Swarm Optimization, PSO) 17
第三章 研究方法 19
3.1模擬運動目標物回波訊號 19
3.2基於粒子群最佳化之最小化熵演算法 22
第四章 實驗結果 24
4.1實驗硬體與環境 24
4.2模擬運動目標物 24
4.3基於粒子群最佳化之最小化熵法 28
第五章 結論與未來發展 33
5.1 結論 33
5.2 未來展望 33
參考文獻 34

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