基于支持向量机的地空通信干扰识别

发布时间：2018-06-17 21:42

本文选题：地空通信信号 + K-均值聚类算法　；参考：《西华大学》2015年硕士论文

【摘要】：随着电磁环境的日趋复杂,无线电民航地空通信频段受干扰事件日益严重,给人们的生命财产安全带来了很大的威胁。如何准确、高效的识别出地空通信异常信号成为无线电日常监测工作的重要目标,同时具有很高的理论价值及研究意义。地空通信业务一般是语音通信,具有偶发性、出现概率低、危害性强的特点。因此在地空通信异常信号识别中,有效利用直观的语音信息,选择合适的分类器成为准确、快速、高效、自动识别地空通信异常信号的关键。K-均值聚类算(K?means)法在信号特征处理及信号识别中已经得到了广泛的应用。但是该算法由于聚类中心初始化问题的存在,使得最终识别效率稳定性无法得到保证。而支持向量机(SVM)则擅长于解决复杂的信号分类问题,在图像处理、医学研究等领域应用广泛。本文将对基于智能优化算法的支持向量机参数选择方法做进一步的研究,给出一种识别效率高、消耗时间短的支持向量机分类器,并将其运用到地空通信干扰信号识别当中。具体研究内容如下:1.探究将无线电地空通信音频信号作为无线电地空通信异常信号识别依据的可行性。利用meansK?算法完成对地空通信音频信号特征集合的构建;通过欧式距离判别法进行了判别实验。2.提出一种基于引力搜索算法的支持向量机分类器。根据支持向量机分类原理及最优化理论建立支持向量机参数选择的优化模型,依据该模型完成上述分类器的构建。3.将上述分类器用于地空通信音频信号的识别当中。通过对比实验,证明该方法具有识别率高、耗时短等特点。
[Abstract]:With the increasing complexity of electromagnetic environment, the interference in the frequency band of radio civil aviation ground-to-air communication is becoming more and more serious, which brings a great threat to the safety of people's life and property. How to accurately and efficiently identify the abnormal signals of ground-to-air communication has become an important target in the daily monitoring of radio, and has high theoretical value and research significance at the same time. Ground-to-air communication service is usually voice communication, which has the characteristics of accidental occurrence, low probability of occurrence and strong harmfulness. Therefore, in the recognition of abnormal signals in ground-to-air communication, it becomes accurate, fast and efficient to use the intuitionistic speech information effectively and select the appropriate classifier. The key method of automatic recognition of abnormal signals in ground-to-air communications. The K-Means clustering method has been widely used in signal feature processing and signal recognition. However, due to the problem of clustering center initialization, the stability of the final recognition efficiency can not be guaranteed. Support Vector Machine (SVM) is good at solving complex signal classification problems, and is widely used in image processing, medical research and other fields. In this paper, the parameter selection method of support vector machine based on intelligent optimization algorithm is further studied, and a support vector machine classifier with high recognition efficiency and short time consumption is proposed and applied to ground to air communication interference signal recognition. The specific contents of the study are as follows: 1. To explore the feasibility of using radio ground-air communication audio signal as the basis for the identification of radio ground-air communication abnormal signals. Using Means K? The algorithm is used to construct the audio signal feature set of ground-to-air communication, and the Euclidean distance discriminant method is used in the discriminant experiment. 2. A support vector machine classifier based on gravitational search algorithm is proposed. According to the classification principle of support vector machine and the optimization theory, the optimization model of parameter selection of support vector machine is established, and the construction of the classifier. 3 is completed according to the model. The classifier is used in the recognition of the ground-air communication audio signal. The comparison experiment shows that the method has the advantages of high recognition rate and short time consuming.
【学位授予单位】：西华大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN972;TP18

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