CAJ | 학술논문

为了提高数字信号调制模式识别在低信噪比下的正确率，根据对边际谱和多重分形理论原理的分析，提出了一种新的基于多重分形理论的特征提取方法。该方法首先引入HHT变换求得样本的边际谱，不同调制模式的边际谱具有明显的差异性，可以利用分形的方法提取边际谱的分形维数作为调制识别的特征参数。最后利用支持向量机分类器进行信号的分类识别。并在求解支持向量机优化问题中，利用通用的粒子群算法确定了最优系数。计算机仿真研究证明，新方法提取的特征能有效地提高识别正确率，具有较好的工程应用性。
위료제고수자신호조제모식식별재저신조비하적정학솔，근거대변제보화다중분형이론원리적분석，제출료일충신적기우다중분형이론적특정제취방법。해방법수선인입HHT변환구득양본적변제보，불동조제모식적변제보구유명현적차이성，가이이용분형적방법제취변제보적분형유수작위조제식별적특정삼수。최후이용지지향량궤분류기진행신호적분류식별。병재구해지지향량궤우화문제중，이용통용적입자군산법학정료최우계수。계산궤방진연구증명，신방법제취적특정능유효지제고식별정학솔，구유교호적공정응용성。
Through the analysis of the marginal spectrum and multifractal theory, a new feature extraction method based on multifractal theory was proposed to improve the accuracy of the digital modulation recognition under the low signal?to?noise ratio. First, the Hilbert?Huang transform was put forward to obtain the marginal spectrum of the samples. There are differences among different modulation modes. The fractal dimensions of the sample after Hil?bert?Huang transform were calculated by the fractal method. Next, the feature was extracted. Finally, the identifica?tion task was solved by using SVM classification machine. In order to determine the optimal coefficient of the sup?port vector machine, a universal particle swarm optimization algorithm was used. The computer simulation results showed that the performance of this feature extracted by the new algorithm efficiently improves the accuracy of mod?ulation recognition and could be feasible to use in engineering applications.