機載雷達需具備偵測及持續追蹤目標物的能力，其系統可分為搜尋模式及追蹤模式，在搜尋模式中，雷達系統可獲得目標物的位置資訊，而雷達所接收的目標物訊號會被地面雜波回波、敵機干擾訊號及雷達接收端的熱雜訊訊號所影響，造成後續追蹤目標物的難度增加。為了抑制地面雜波及敵機干擾訊號，並同時保有目標物訊號，時空雙域訊號處理 (STAP) 針對空間域及時間域同時作訊號處理，即使干擾訊號和目標訊號落在相似的空間位置，也能以時域的訊號處理結果作區分；反之亦然。然而，時空雙域訊號處理所遇到最大的困境，即是其處理的資料維度極大，造成高計算複雜度，因此衍生出降維的時空雙域訊號處理。
　　這篇論文對降維的時空雙域訊號處理進行研究，特別針對ΣΔ− STAP 演算法分析，因其軟硬體架構皆與傳統降維演算法有高度相容性。模擬結果顯示ΣΔ− STAP 演算法能夠有效地抑制雜波並同時保有目標物訊號，使機載雷達能夠持續追蹤目標，且其計算複雜度有明顯的降低。此外，為了瞭解ΣΔ− STAP 演算法的特性，在這篇論文中也針對特定變數以不同設定進行模擬。

Airborne radar systems are required to be capable of detecting and tracking target. In Searching mode, radar system first obtain location information of target. However, the echo of ground clutter, jamming from enemies, and thermal noise at receiver degrades the performance of radars, causing the difficulty of target tracking. Space-time adaptive processing (STAP) is a technique for suppressing the interference, meanwhile it can also preserve the received signal of target. STAP perform signal processing in both space-domain and time domain. Thus, even if the target and the interference source fall in near location, STAP can still separate the received signal from time-domain, and vice versa. Nevertheless, high computational complexity is the main challenge of fully STAP. To solve the issue, reduced dimension STAP is derived.
In the thesis, a research of reduced-dimension STAP will be discussed, especially focus on ΣΔ−STAP algorithm, due to its compatibility of both hardware and software with the traditional reduced-dimension STAP. This thesis shows that ΣΔ−STAP algorithm can suppress the clutter signal and preserve the received signal of target at the same time. And we also analyze the computational complexity of Fully STAP and ΣΔ−STAP, which show that the computational complexity of ΣΔ−STAP is significantly reduced. Besides, the thesis also apply different settings to specific parameters in order to gain a better understanding of the algorithm.

中文摘要
英文摘要
目錄
圖目錄
表目錄
第一章 緒論-------------------------------------------1
第二章 系統模型---------------------------------------5
第三章 系統架構---------------------------------------22
第四章 Monopulse Radar-------------------------------28
第五章 ΣΔ-STAP---------------------------------------33
第六章 模擬結果---------------------------------------45
第七章 結論-------------------------------------------62
參考文獻

[1] A. D. Brown, Electronically scanned arrays: MATLAB modeling and simulation. CRC Press, 2012, pp. 1–79.
[2] T. B. Hale, “Airborne radar interference suppression using adaptive three-dimensional techniques,” p. 263, 06 2002.
[3] J. Ward, “Space-time adaptive processing for airborne radar,” in 1995 International Conference on Acoustics, Speech, and Signal Processing, vol. 5, May 1995, pp. 2809–2812.
[4] B. R. Mahafza, Radar Signal Analysis and Processing Using MATLAB. CRC Press,2009, pp. 353–357.
[5] J. A. S. William L. Melvin, Principles of Modern Radar: Radar Applications, Volume 3. SciTech Publishing, 2013, pp. 175–226.
[6] W. A. H. Mark A. Richards, James A. Scheer, Principles of Modern Radar: Basic Principles, Volume 1. SciTech Publishing, 2010, pp. 175–226.
[7] M. A. Richards, Fundamentals of Radar Signal Processing. McGraw-Hill Professional, 2005, pp. 286–303. [Online]. Available: https://mhebooklibrary.com/doi/book/10.1036/0071444742
[8] J. Ward., “Space-Time Adaptive Processing for airborne radar,” Contract F19628-95-C-0002, Lincoln Laboratory, Massachusetts Institute of Technology, Lexing ton, Massachusetts, December 1994.
[9] Özgür Eryiğit, “Interference suppression by using space-time adaptive processing for airborne radar,” Master’s thesis, Middle East Technical University, 2008.
[10] M. C. Wicks, M. Rangaswamy, R. Adve, and T. B. Hale, “Space-time adaptive processing: a knowledge-based perspective for airborne radar,” IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 51–65, Jan 2006.
[11] M. D. He and J. S. Cao, “Recursive ka-stap algorithm based on qr decomposition,” in 2013 International Workshop on Microwave and Millimeter Wave Circuits and System Technology, Oct 2013, pp. 391–394.
[12] X. H. Weike Feng, Yongshun Zhang, “Complexity reduction and clutter rank estimation for mimo-phased stap radar with subarrays at transmission,” Digital Signal Processing, vol. 60, pp. 296–306, 2016.
[13] H.-W. L. Xaoming Li, Dazheng Feng and D. Luo, “Dimension-reduced space-time adaptive clutter suppression algorithm based on lower-rank approximation to weight matrix in airborne radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 50, no. 1, pp. 53 – 69, Jan 2014.
[14] T. L. Yongxu Liu, Xiaopeng Yang, “New hybrid stap algorithm of d3 and stmb for discrete interference suppression in nonhomogeneous clutter,” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Nov 2010.
[15] F.-K. Bi, D.-Y. Zhang, L. L. Xi-Chang Cai, and Y.-X. Liu, “Fast reduced-rank stap algorithm based on gram–schmidt orthogonalisation for airborne radar,” International Journal of Electronics, vol. 102, pp. 1382–1393, 2015.
[16] Z. Wang, Y. Wang, K. Duan, and W. Xie, “Subspace-augmented clutter suppression technique for stap radar,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 3, pp. 462 – 466, Mar 2016.
[17] R. Brown, R. Schneible, M. Wicks, H. Wang, and Y. Zhang, “Stap for clutter suppression with sum and difference beams,” IEEE Transactions on Aerospace and Electronic Systems, vol. 36, no. 2, pp. 634 – 646, Apr 2000.
[18] D. K. B. Sam uel M. Sherman, Monopulse Principles and Techniques. Artech House, 2011, pp. 1–31.