研究生: |
徐秉希 Ping-Hsi Hsu |
---|---|
論文名稱: |
最小相移鍵控調變脈衝串之起始點偵測研究 Research on The Detection of The Beginning of an MSK Modulated Pulse Train |
指導教授: |
劉馨勤
Hsin-Chin Liu |
口試委員: |
張立中
Li-Chung Chang 吳玉龍 YU-LUNG WU 林俊霖 Chun-Lin Lin |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電機工程系 Department of Electrical Engineering |
論文出版年: | 2020 |
畢業學年度: | 108 |
語文別: | 中文 |
論文頁數: | 67 |
中文關鍵詞: | 最小相移鍵控調變 、起始點檢測 、機器學習 、分類 |
外文關鍵詞: | MSK modulation, Signal starting point detection, Machine learning, Classification |
相關次數: | 點閱:195 下載:0 |
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本論文提出一種基於機器學習對最小相移鍵控調變脈衝串訊息的訊號起始點檢測方法。訊號端點檢測是訊號處理中一個十分重要的技術,主要可以分為特徵提取和閾值設置兩個步驟,通過接收訊號提取出的特徵將訊號從雜訊環境中區分為訊號段與非訊號段,並且使用閾值設置來找出訊號段的起始點和終止點。但在實際應用時常因為環境的不同,造成閾值不易設置,而且不同的訊號也會使設置的條件難以統一。
本論文結合特徵提取與機器學習分類器對最小相移鍵控調變脈衝串的訊息進行訊號起始點檢測。將接收訊息提取出特徵,並且透過預處理技術和特徵選擇方法選擇出評分最好的特徵子集,再經由模型選擇找出最適合訊息的分類器演算法。為此我們可以知道對於最小相移鍵控調變脈衝串訊息,特徵與分類器演算法的最佳組合,以求得最佳化的分類效能。最後透過分類訊息的訊號段與非訊號段,利用演算法找出訊息的起始點。
本論文利用模擬軟體MATLAB模擬最小相移鍵控調變脈衝串的訊息,並且使用Python編程語言以及機器學習軟件庫Scikit-learn中的分類器演算法,進行本論文方法的功能驗證與效能分析,最後探討訊息中含有不同訊雜比對於訊號起始點檢測的影響。模擬結果顯示最小相移鍵控調變脈衝串訊息在不同訊雜比的環境下,使用最佳的特徵與分類器演算法的組合,對於訊號起始點檢測有著較低的估測誤差。
This paper proposes a method for detecting the signal starting point of an MSK modulated pulse train based on machine learning. Signal endpoint detection is a very important technology in signal processing. It can be divided into two steps: feature extraction and threshold setting. The features extracted from the received signal separate the signal from the noise environment into signal and non-signal segments. And use the threshold setting to find the start point and end point of the signal segment. However, in practical applications, it is often difficult to set the threshold due to different environments, and different signals also make it difficult to unify the setting conditions.
This paper combines feature extraction and machine learning classifier to detect the signal starting point of an MSK modulated pulse train. The features of the received message are extracted, and the subset of features with the best score is selected through preprocessing technology and feature selection method, and then the most suitable classifier algorithm for the message is found through model selection. For this reason, we can know the best combination of features and classifier algorithm for the minimum phase shift keying modulation burst message to obtain the best classification performance. Finally, by classifying the signal segment and non-signal segment of the message, an algorithm is used to find the starting point of the message.
This paper uses the simulation software MATLAB to simulate the message of the minimum phase shift keying modulation pulse train, and uses the Python programming language and the classifier algorithm in the machine learning software library Scikit-learn to perform the function verification and performance analysis of the method in this paper. Finally, the influence of different signal-to-noise ratios in the message on the detection of the signal starting point is discussed. The simulation results show that the minimum phase shift keying modulated burst message uses the best combination of features and classifier algorithms in environments with different signal-to-noise ratios, and has a lower estimation error for the signal onset detection.
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