本文基於自我注入鎖定(Self-Injection Locking, SIL)技術運用於合成孔徑雷達(Synthetic Aperture Radar)，將SIL雷達架設於等速移動平台，達到SAR的量測效果，並利用距離都卜勒演算法提供二維成像與目標物定位。透過SIL雷達高靈敏度的特性，檢測雷達慢時間相位下所包含的目標都卜勒訊息。本文利用目標的都卜勒訊息搭配合成孔徑雷達的二維成像資訊，能提取出具生理徵象的目標，並移除不具生理徵象的靜態物體來達到生理成像的效果。本文根據人數與環境的不同分別進行了單及多人體的生理定位與成像，並透過在目標與雷達間擺置木牆來證明本文所使用的系統具有穿牆生理成像的能力。
本文也將SIL雷達運用於干涉合成孔徑雷達(InSAR)之上，利用兩軌道間彼此的慢時間相位差搭配兩軌道間的高度差，進行目標與雷達間的相對高度換算，透過已知的雷達軌道高度即可還原目標的真實高度。透過二維成像所提供的定位資訊與InSAR所提供的高度資訊，以實現多目標的三維定位。

This thesis presents a Synthetic Aperture Radar (SAR) based on self-injection-locking (SIL) technology. The SIL radar was mounted on a constant-speed platform to perform SAR measurements. Then, the 2D imaging and target location are obtained using range Doppler algorithm (RDA). Moreover, the Doppler information of the targets can be extracted from the slow-time phase measured by the high-sensitivity SIL radar. This study preserves the images of moving targets but removes those of static objects in the SAR measurements by using the targets’ Doppler information. This study not only conducts experiments to provide the positions and images of the single or multiple targets under different indoor conditions, but also demonstrates the through-wall imaging by placing a wood wall between the targets and radar.
This system also operates as an Interferometric synthetic aperture radar (InSAR). The height between the targets and radar can be calculated in terms of the slow-time phase difference between two radar trajectories, and the actual height of the targets can be estimated from adding the height of the radar. This thesis finally demonstrates the capability of 3D positioning and using the information provided from InSAR and 2D SAR measurements.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 xi
第一章 序論 1
1.1 研究背景與動機 1
1.2 調頻連續波雷達簡介 2
1.3 合成孔徑雷達(SAR) 4
1.4 干涉式合成孔徑雷達(InSAR) 6
1.5 自我注入鎖定雷達 8
1.6 章節規劃 9
第二章 系統整合與實現 10
2.1 系統架構 10
2.2 SAR演算法 13
2.3 移除靜態物體方法與流程 21
2.4 InSAR演算法 26
第三章 SAR成像實驗結果與分析 28
3.1 單目標位置辨識 28
3.1.1 大金屬板與待測物相距2個range bin 30
3.1.2 無雜波環境下單人位置辨識 30
3.1.3 有雜波環境下單人位置辨識 32
3.2 靈敏度測試 35
3.2.1 致動器 35
3.2.2 不同人體姿態 37
3.3 雜波環境下多目標辨識 41
3.3.1 多目標辨識 41
3.3.2 多人辨識 45
3.4.1 木牆與待測物相距2個range bin 47
3.4.2 木牆與待測物相距1個range bin 49
3.4.3 人體穿牆實驗 50
3.4.4 多目標穿牆定位 52
4.1雷達最佳感測範圍 55
4.2 單目標三維定位 58
4.3 多目標三維定位 60
4.4 誤差探討 62
第五章 結論與未來展望 64
參考文獻 66

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