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研究生:黃威誠
研究生(外文):Wei-Cheng Huang
論文名稱:基於高階累積量的自動調變 識別系統之研究
論文名稱(外文):Research on Automatic Modulation Classification System Based on Higher Order Cumulants
指導教授:張立中張立中引用關係
指導教授(外文):Li-Chung Chang
口試委員:曾恕銘劉馨勤曾德峰張立中
口試委員(外文):Shu-Ming TsengHsin-Chin LiuDer-Feng TsengLi-Chung Chang
口試日期:2018-07-26
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:82
中文關鍵詞:自動調變識別系統高階累積量訊號特徵辨識正確率複雜度
外文關鍵詞:automatic modulation classificationHigher Order CumulantFeature-basedprobability of correct classificationComplexity
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在通訊領域中有許多應用需要先檢測訊號的調變類型,例如:頻譜監控和電子戰等等,隨著數位調變種類越來越多,複雜性也越來越高,自動調變識別(automatic modulation classification)系統進而變得越來越重要。
自動調變辨識系統的作用在於檢測接收到的未知訊號並辨識其調變類型,即可對此信號進行解調,進而得到訊號中傳輸的內容,此一技術在軍事情報領域中就顯得相當的重要。
自動調變辨識系統常見的有兩種方法,第一種為最大似然法(Maximum Likelihood, ML),第二種是基於訊號特徵(Feature-based, FB)的方法。而高階累積量(Higher Order Cumulants, HOCs)屬於FB的方法之一,高階累積量的特性為訊號經過可加性高斯白噪聲(AWGN)通道後,仍能辨識出未知調變的訊號種類。
本論文將會比較7篇使用HOCs的不同辨識系統,在能成功辨識出12種調變以及多載波訊號的條件下,提出提升平均正確辨識率系統以及降低複雜度方法之辨識系統,再與7篇原始辨識系統進行效能比較。
For many applications in the field of communication, such as spectrum surveillance and electronic warfare, which requires to detect the modulation type of a given communication signal. With the increasing variety of digital modulation and complexity, the automatic modulation classification system has become more and more important.
The function of the automatic modulation identification system is to detect the received unknown signal and identify its modulation type, and then demodulate the signal to obtain the content transmitted in the signal. This technology is quite important in the field of military intelligence.
There are two common methods for automatic modulation identification systems. First one is Maximum Likelihood (ML), and second one is based on Feature-Based (FB). Higher Order Cumulants (HOCs) is one of the methods using FB. The advantage of HOCs is even the signal pass the additivity white Gaussian noise (AWGN) channel, the unknown modulation signal type can still be identified by HOCs.
In this paper, we compare 7 different identification systems using HOCs. Under the condition that 12 kinds of modulation type and multi-carrier signals can be successfully identified, the best probability correct classification system and the lowest complexity classification system are proposed. Then we compare the classification system we proposed to the original classification system.
摘要 I
ABSTRACT II
目錄 IV
圖目錄 VI
表目錄 VIII
第1章 序論 1
1.1 研究動機與目的 1
1.2 論文架構 2
第2章 相關理論介紹與文獻回顧 3
2.1 自動調變辨識系統(Automatic Recognition of Digital Modulation) 3
2.1.1 發展概況 3
2.1.2 基本架構 3
2.2 特徵擷取 4
2.2.1 調變訊號數學模型 4
2.2.2 高階累積量(Higher Order Cumulants, HOCs) 5
2.3 分類器做法 10
2.3.1 人工神經網路 (Artificial Neural Networks, ANNs) 分類器 10
2.3.2 支援向量機(SVM)分類器 10
2.3.3 決策樹分類器 11
第3章 提出的系統架構 12
3.1 多載波訊號與FSK訊號辨識介紹 12
3.1.1 多載波訊號辨識 12
3.1.2 FSK訊號辨識 12
3.1.3 提出對於多載波訊號以及FSK訊號的辨識方法 17
3.2 提升平均辨識率方法 18
3.2.1 七篇辨識系統決策樹介紹 18
3.2.2 七篇辨識系統決策樹方法之平均正確辨識率計算 31
3.2.3 基於平均正確辨識率最高的路徑選擇 32
3.2.4 路徑結合之提升平均辨識率系統 44
3.3 降低複雜度方法 46
3.3.1 七篇辨識系統中三群調變訊號辨識路徑之複雜度計算 46
3.3.2 基於計算複雜度最低的路徑選取 48
3.3.3 路徑結合之低平均複雜度系統 48
第4章 模擬結果與討論 50
4.1 辨識率效能分析 50
4.1.1 辨識單載波與多載波系統之效能 50
4.1.2 提升平均正確辨識率方法與7種原始決策樹共有調變的效能比較 51
4.2 複雜度分析 57
4.2.1 降低平均複雜度的方法與七篇原始決策樹的效能比較 57
第5章 結論與未來研究方向 65
附錄A 68
參考文獻 69
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