基于宽带时频图的多路摩尔斯信号自动检测
发布时间：2019-08-01 15:41
【摘要】：针对现有算法没有涉及的宽带环境下多路摩尔斯信号自动检测问题,提出一种基于宽带时频图和集成学习分类器的算法.首先,通过提出一种基于宽带时频图的信号快速窄带滤波方法,实现噪声背景中各类型信号窄带时频图的快速获取;然后,为从上述窄带时频图中识别出多路摩尔斯信号,提出3个新特征与局部二值模式特征构成特征向量;最后,采用集成学习算法设计分类器实现摩尔斯信号的自动检测.与现有算法对比实验结果表明:针对多组实际数据,该算法的正确率均可达95%以上,同时误检率低于10%,具有良好的鲁棒性和应用价值.
[Abstract]:In order to solve the problem of automatic detection of multi-channel Morse signals in broadband environment, an algorithm based on broadband time-frequency graph and integrated learning classifier is proposed. Firstly, a fast narrowband filtering method based on broadband time-frequency graph is proposed to obtain the narrowband time-frequency graph of each type of signal in noise background. Then, in order to recognize the multi-channel Morse signal from the narrowband time-frequency graph, three new features and local binary pattern features are proposed to form the feature vector. Finally, the integrated learning algorithm is used to design a classifier to realize the automatic detection of Morse signal. Compared with the existing algorithms, the experimental results show that the accuracy of the algorithm can reach more than 95% and the error detection rate is less than 10% for multiple groups of actual data, so it has good robustness and application value.
【作者单位】： 先进信息网络北京实验室;北京工业大学信息学部;
【基金】：国家自然科学基金资助项目(61672064) 北京市自然科学基金资助项目(KZ201610005007)
【分类号】：TN925;TP391.41
，
本文编号：2521841
[Abstract]:In order to solve the problem of automatic detection of multi-channel Morse signals in broadband environment, an algorithm based on broadband time-frequency graph and integrated learning classifier is proposed. Firstly, a fast narrowband filtering method based on broadband time-frequency graph is proposed to obtain the narrowband time-frequency graph of each type of signal in noise background. Then, in order to recognize the multi-channel Morse signal from the narrowband time-frequency graph, three new features and local binary pattern features are proposed to form the feature vector. Finally, the integrated learning algorithm is used to design a classifier to realize the automatic detection of Morse signal. Compared with the existing algorithms, the experimental results show that the accuracy of the algorithm can reach more than 95% and the error detection rate is less than 10% for multiple groups of actual data, so it has good robustness and application value.
【作者单位】： 先进信息网络北京实验室;北京工业大学信息学部;
【基金】：国家自然科学基金资助项目(61672064) 北京市自然科学基金资助项目(KZ201610005007)
【分类号】：TN925;TP391.41
，
本文编号：2521841
本文链接：https://www.wllwen.com/kejilunwen/xinxigongchenglunwen/2521841.html
