基于时间序列分解的信号挖掘与预测  

Signal mining and prediction based on time series decomposition

作　　者：郭锦桥 柳禹名 曹卫东 林云[1] GUO Jinqiao;LIU Yuming;CAO Weidong;LIN Yun(School of Communication and Information Engineering,Harbin Engineering University,Harbin Heilongjiang 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《太赫兹科学与电子信息学报》2023年第6期751-758,共8页Journal of Terahertz Science and Electronic Information Technology

摘　　要：随着电磁信号环境日趋复杂以及通信设备数量的不断增加,电磁信号受到干扰问题逐渐加剧。因此,对于信号在不同噪声环境下的接收与处理技术的研究以及在复杂的电磁环境中对信号各项数据指标及其携带信息的利用十分关键。为了解在不同电磁环境下含噪信号的性能表现,提高信号的利用质量及可靠性,本文提出一种基于时间序列分解的电磁数据处理方法。建立了基于加法季节性时间序列分解的含噪信号处理模型,并利用该模型对信号在有噪环境下的表现与规律性、趋势、误码率等性能进行分析与评估,对原始信息、载波信息进行挖掘预测。与传统方法相比,本文提出的基于时间序列分解的信号挖掘与预测模型在高噪环境下对信号预测更为准确。With the increasing complexity of the electromagnetic signal environment and the increasing number of communication devices,the interference with electromagnetic signals is gradually increasing.Therefore,the study on signal reception and processing techniques in different noise environments and the use of signal data indicators and the information they carry in complex electromagnetic environments is very critical.In order to understand the performance of noisy signals in different electromagnetic environments and improve the quality and reliability of signal utilization,a time series decomposition-based electromagnetic data processing method is proposed.A noisy signal processing model is established based on additive seasonal time series decomposition,and the model is also employed to analyze and evaluate the performance of signals in noisy environments with regularity,trend,BER,etc.,and to data-mine the original information and carrier information.Compared with the traditional methods,the proposed time series decomposition-based signal mining and prediction model is more accurate for signal prediction in noisy environment.

关 键 词：时间序列分解 特征提取 数据挖掘 数据预测 机器学习 

分 类 号：TN97[电子电信—信号与信息处理]