使用機器學習技術提升以輔助波束對 方法做相位陣列天線的角度估計

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姓名

周昌平(Chang-Pin Chou) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

使用機器學習技術提升以輔助波束對方法做相位陣列天線的角度估計
(Machine Learning Techniques for Phased Array Antennas DOA Estimation Based on The Auxiliary Beam Pair Method)

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摘要(中)

近年來隨著科技的進步，無人機系統也逐漸發展完善，
各產業也爭相將此投入各個產業以及軍事方面，因此勢必需要對
此進行研究和追蹤。此篇論文將著重以輔助波束對(Auxiliary Beam
Pair) 演算法追蹤無人機與基站之間的角度，但演算法在均勻平面
天線陣列下只能得到近似角度，因此對這部分提出以前一個取樣
時間，用演算法算出的角度當作下一個取樣點的視軸(Boresight)
來降低近似誤差，也希望可以利用其他方式再降低誤差，所以會
加入擴展卡曼濾波器修正近似的問題，也使用機器學習修正輔助
波束對誤差的參數，再代回演算法求出角度，並比較這些方法在
不同環境下的效果，最後再針對結果分析各方法的優缺點。

摘要(英)

In recent years, with the advancement of technology, unmanned
aerial vehicle (UAV) systems have become increasingly sophisticated.
Various industries, including military applications, have been eager to adopt UAVs. Consequently, there is a growing need for research and tracking of UAVs. This paper focuses on using the Auxiliary Beam Pair algorithm to track the angle between a UAV and a base station. However, when using a uniform planar array, the algorithm can only obtain an approximate angle. To mitigate this approximation error, we propose using the angle calculated by the algorithm at the previous sampling time as the boresight for the next sampling point. Additionally, we aim to further reduce the error by incorporating a Extended Kalman filter to correct the
approximation and by using machine learning to adjust the parameters of the Auxiliary Beam Pair algorithm. The angles are then recalculated using the algorithm, and the performance of these methods under different environments is compared. Finally, the advantages and disadvantages of each method are analyzed based on the results.

關鍵字(中)

★ 均勻平面天線陣列
★ 波束成形
★ 波束搜索
★ 角度估計
★ 輔助波束對
★ 擴展型卡爾曼濾波器
★ 深度神經網路
★ 卷積神經網路

關鍵字(英)

★ UPA
★ Beamforming
★ Beam searching
★ DOA estimation
★ ABP
★ EKF
★ DNN
★ CNN

論文目次

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . ii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . i
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . ii
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . iv
第1 章序論. . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 章節架構. . . . . . . . . . . . . . . . . . . . . . . 3
第2 章系統設計與模型. . . . . . . . . . . . . . . . . . . . 4
2.1 相位陣列天線基本架構. . . . . . . . . . . . . . . . . . 4
2.2 通訊系統架構. . . . . . . . . . . . . . . . . . . . . 7
2.2.1 波束控制(Beam steering) . . . . . . . . . . . . . . 7
2.2.2 接收端訊號處理. . . . . . . . . . . . . . . . . . . 8
2.3 空間特徵檢測. . . . . . . . . . . . . . . . . . . . . 9
第3 章角度搜索並追蹤. . . . . . . . . . . . . . . . . . . .12
3.1 接收波束訊號. . . . . . . . . . . . . . . . . . . . . 12
3.2 輔助波束對. . . . . . . . . . . . . . . . . . . . . . 13
3.2.1 估計誤差. . . . . . . . . . . . . . . . . . . . . . 19
3.2.2 降低誤差. . . . . . . . . . . . . . . . . . . . . . 20
3.3 擴展卡爾曼濾波器. . . . . . . . . . . . . . . . . . . 21
3.3.1 EKF 基於ABP . . . . . . . . . . . . . . . . . . . . 25
3.4 機器學習. . . . . . . . . . . . . . . . . . . . . . . 27
3.4.1 深度神經網路. . . . . . . . . . . . . . . . . . . . 27
3.4.2 捲積神經網路. . . . . . . . . . . . . . . . . . . . 31
第4 章機器學習性能比較. . . . . . . . . . . . . . . . . . .34
4.1 DNN . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 CNN . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.1 性能比較. . . . . . . . . . . . . . . . . . . . . . 54
第5 章模擬結果與分析. . . . . . . . . . . . . . . . . . . 59
5.1 每次接收單筆. . . . . . . . . . . . . . . . . . . . . 61
5.2 每次接收1000 筆. . . . . . . . . . . . . . . . . . . . 65
第6 章結論. . . . . . . . . . . . . . . . . . . . . . . . 70
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . 71

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指導教授

張大中(Dah-Chung Chang)

審核日期

2025-1-22

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