基于S变换的通信电台个体特征提取与识别新方法研究

项目名称： 基于S变换的通信电台个体特征提取与识别新方法研究

项目编号： No.61301095

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 林云

作者单位： 哈尔滨工程大学

项目金额： 30万元

中文摘要： 通信电台个体识别是通信对抗中非常重要的研究课题，而特征提取和识别器设计是其两个最关键的技术。一方面，针对一般时频特征提取技术存在的时频分辨率受限、时频聚焦性差、交叉项干扰、核函数复杂等缺点，引入一种全新的时频分析技术-S变换，推导噪声在S变换域的统计特征，分析噪声和信号在S变换域的特性，提出一种自适应恒虚警检测模型和三个自适应时频滤波模型，该模型具有更高的检测概率和信噪比。在此基础上，提出从信号S域时频谱中提取通信电台调制特征和指纹特征的方法，该方法提取的特征具有更好的可检测性、稳定性和可靠性；另一方面，针对单一分类器识别效果受限的缺点，从分析识别特征出发，结合灰色关联分析和证据融合的基本思想，提出一种区间证据识别器的设计方法，该识别器在保证稳定和可靠的前提下，具有更高的识别率。本课题的研究成果旨在低信噪比和信噪比大范围变化情况下提高通信电台的个体识别率，并对相关学科的发展产生积极的影响

中文关键词： 通信信号处理；S变换；信号检测与滤波；特征提取技术；分类识别技术

英文摘要： The problem of individual recognition for communication station is a very important research subject in communication countermeasure, and the extraction of recognition feature and the design of individual recognizer are two key technologies for it. In one hand, the technology of ordinary Time-Frequency analysis has some defects, such as the limitation of Time-Frequency resolution, the weakness of Time-Frequency focusing, the interference of cross-term, the complexity of kernel function, and so on. In order to overcome those defects, a new technology of Time-Frequency analysis, which is usually called as S-Transform, is introduced in this subject. Through deducing the statistical characteristic of noise and analyzing the features of signal and noise in S-Transform domain, a new model of Adaptive Constant False-Alarm Detection and three new models of Adaptive Time-Frequency Filtering are presented in this subject, those new models will have better detection probability and signal-to-noise ratio in the case of Constant False-Alarm Rate than other Time-Frequency analysis technologies. Based on the research result mentioned above, some new methods of how to extract the modulation feature and fingerprint feature of communication station in S-Transform domain are presented, those new methods will have better testabilit

英文关键词： Communication Signal Processing；S-Transform；Signal Detection and Filtering；The Technology of Feature Extraction；The Technology of Classfication and Recognition