摘要
研究了GNSS导航接收机在干扰源条件下的干扰检测和识别技术。除常见的卫星干扰如脉冲干扰、扫频干扰、BPSK干扰、宽带高斯白噪声干扰外,还添加了QPSK和8PSK干扰进行建模分析,并提取了常见的特征参数,如信号3dB带宽和频谱峰度等。通过研究,高阶累积量被提取作为MPSK干扰类内识别的特征参数。研究了基于决策树和BP神经网络等分类器算法的干扰盲识别,并分析比较了这些分类算法的识别准确率,为抗干扰领域的研究提供了重要参考。
This paper studies the interference detection and recognition technology of GNSS navigation receiver under interference source conditions.In addition to common satellite interferences such as pulse interference,sweep interference,BPSK interference and broadband gaussian white noise interference,QPSK and 8PSK interference are added for modeling analysis,and common characteristic parameters such as 3dB bandwidth and spectrum kurtosis of the signal are extracted.Through research,the high-order cumulant is extracted as the characteristic parameters recognized within MPSK interference.The decision tree algorithm and BP neural network algorithm are adopted for blind identification of interference,and the recognition accuracy of these classification algorithms are studied,analyzed and compared.The results show that the methods can provide important references in the research field of anti-interference.
作者
王佳欣
常青
田原
黄坚
WANG Jia-xin;CHANG Qing;TIAN Yuan;HUANG Jian(School of Electronic Information Engineering,Beihang University,Beijing 100191,China)
出处
《导航定位与授时》
2020年第4期117-122,共6页
Navigation Positioning and Timing
基金
装备预研领域基金(61405180203)。
关键词
干扰检测与识别
特征提取
决策树
BP神经网络
Interference detection and identification
Feature extraction
Decision tree
BP neural network
