基于循环平稳特征的分阶段直扩超宽带信号检测  

Phased DS-UWB Signal Detection Based on Cyclic Stationary Characteristics

作　　者：王晓蓉 宋晓鸥[1] WANG Xiaorong;SONG Xiaoou(School of Information Engineering,Engineering University of PAP,Xi′an 710086,China)

机构地区：[1]武警工程大学信息工程学院,西安710086

出　　处：《电讯技术》2020年第9期1075-1079,共5页Telecommunication Engineering

基　　金：国家自然科学基金资助项目(61801516)。

摘　　要：超宽带(Ultra Wideband,UWB)信号检测对实现超宽带定位具有重要意义。针对低信噪比下直扩超宽带信号(Direct Sequence Spread Spectrum UWB,DS-UWB)功率谱密度极低,难以通过传统手段检测的问题,提出了一种基于循环平稳特征的分阶段直扩超宽带信号检测算法。在介绍直扩超宽带的信号模型的基础上,提出了改进循环平稳特征检测方法,分析了二元假设两种情形下灰度图的差异,利用这种差异将两类图像放入卷积神经网络(Convolutional Neural Network,CNN)自行训练提取特征,进而利用训练好的网络进行检测。为更高效检测出信号,采用分阶段检测,即第一阶段先进行能量检测,若未检测到,则进入第二阶段改进循环平稳特征检测。仿真结果表明,在信噪比小于-8 dB时,所提方法的检测性能明显优于传统的循环平稳特征检测。Ultra-wideband(UWB)signal detection is of great significance for UWB positioning.For the problem that the power spectrum density of direct sequence spread spectrum UWB(DS-UWB)signal is very low at low signal-to-noise ratio(SNR)and it is difficult to detect the signal by traditional means,a phased DS-UWB signal detection algorithm based on cyclic stability is proposed.Firstly,the signal model of DS-UWB is introduced,and an improved cyclic stationary feature detection method is proposed to analyze the difference of gray images under binary hypothesis.According to the difference,the two types of images are put into a convolutional neural network(CNN)to extract features by self-training,and then the trained network is used for detection.Furthermore,in order to detect signals more efficiently,energy detection is carried out at first.If no signal is detected,the improved cyclic stationary feature detection is adopted in the second stage.Simulation results show that the detection performance of the proposed method is significantly better than that of the traditional cyclic stationary feature detection when the SNR is less than-8 dB.

关 键 词：直扩超宽带信号 能量检测 循环平稳 卷积神经网络 

分 类 号：TN925[电子电信—通信与信息系统]