基于谱图和神经网络的通信干扰模式识别方法

Pattern recognition method of communication interference based on power spectrum density and neural network

对通信系统受干扰的模式进行分析和模式识别,可以指导通信系统进行相应的自适应参数调整,以具有更强、更有针对性的抗干扰能力。研究宽带通信系统,利用多隐藏层的神经网络可以解决任意形式分类问题的特性,构建一种基于功率谱谱图和双隐藏层神经网络的通信干扰模式识别方法,可以对5种常见的通信干扰进行快速的模式识别。仿真结果表明,该通信干扰模式识别方法对干扰模式在不同的干噪比情况下能获得99.6%以上的平均识别概率,对除梳状谱干扰外的各种干扰模式识别准确率均达到99.7%以上,梳状谱干扰识别准确率达到98.4%以上。该方法具备较稳定的识别能力,可应用于干扰感知的流程中。

Analysis and pattern recognition of the interference undergoing in the communication system can assist the self-adaptive adjustment of the communication system parameters,thereby the anti-jamming capability can be stronger and targeted.A wide-bandwidth communication system is researched.Previous research shows that multi-hidden-layer neural network can resolve any form of classification problems.In order to classify the five common interference patterns,a classification method which uses power spectrum density and two-hidden-layer neural networks is proposed.Simulation results show that,under different interference patterns and different Interference-Noise-Ratios(INR),the average recognition accuracy is above 99.6%.In all the other four interference patterns without comb-spectrum interference,the recognition accuracy is above 99.7%,while 98.4%in the comb-spectrum interference.The proposed method has relatively stable recognition ability,and can be applied to the detection of communication interference.