數位調變訊號自動識別之統計特徵演算法

Statistical Feature Based Algorithms for Automatic Recognition of Digital Modulation Signals

戴哲揚(Che-Yang Dai)

指導教授 ： 黃正光

元智大學/工程學院/通訊工程學系/碩士(2006年)

摘要

在本論文中，提出以基於統計特徵演算法來做數位調變訊號之自動辨識，經由統計數位調變訊號之振幅、頻率及相位之變化，作為訊號調變模式判定之參考。研究步驟分成三個部份，首先以產生各類數位調變訊號，並分析及特性。其次，使用類神經網路實現數位調變訊號之辨識。最後，我們提出利用訊號振幅、頻率及相位之機率密度函數統計特徵方法，擷取更多不同訊號特徵。與傳統的類神經網路方法相較，此方法具較佳之可靠性，且容易套用至其它不同類型訊號，除此之外，亦具有其它優點，如在較低訊雜比的條件下，其運算複雜度較低，而仍有較佳之效率。

關鍵字

統計特徵演算法 ； 數位調變辨識

並列摘要

In this thesis, we investigate some statistical feature–based algorithms for automatic recognition of digital modulation signals. In order to judge the type of a modulated signal, we compute some discriminating statistics of the instantaneous amplitude, frequency and phase of the digital modulation signal. There are three steps for the research. First, we generate the different test digital modulation signals and analyze their features. Next, we use the artificial neural network for “automatic recognition of digital modulation signals”. Last, we propose to exploit the probability density function (PDF) of amplitude, frequency and phase to extract more distinguish statistic of the signal’s feature. Comparing with the traditional neural network method, it is more reliable and easy to extend to more different case. In addition, it has other merits like low complexity and superior performance in low SNR environment.

並列關鍵字

statistical feature–based algorithms ； automatic recognition ； digital modulation

參考文獻

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