摘要
短波段数字调幅广播(DRM)利用OFDM调制方式,实现了优异的抗衰落和抗干扰能力。与传统模拟调幅(AM)广播相比,DRM不但节省发射功率,还能提供近似于FM的音质,是未来短波广播的发展趋势。短波广播在经由电离层反射传播时,电离层扰动将对信号造成幅度调制、频率偏移及多径传播等影响,严重影响信号的时域特征和频域特征,进而对信号识别造成不利影响。针对此问题,详细分析了DRM信号的时域自相关特征。定量分析了DRM信号协议中特有的帧结构、循环前缀和增益导频等成分对自相关特征的影响,并通过实测数据对所提出的结论进行了验证。所提出的时域自相关方法具有运算速度快、抗电离层扰动的特点,能有效地应用在DRM信号识别领域,对做好未来短波广播频段的无线电监测有着重要意义。
Digital Radio Mondiale(DRM) utilizes OFDM modulation to achieve excellent anti-fading and anti-interference capabilities.Compared with the traditional analog amplitude modulation(AM) broadcasting,DRM possesses several advantages such as requiring less transmission power and providing.DRM is treated as the future method for shortwave broadcasting.Shortwave broadcasting usually propagates through ionospheric reflection.For the transmitted electromagnetic wavve,the ionospheric disturbances will cause amplitude modulation,frequency shift,and multipath propagation effects,which will seriously affect the time-domain and frequency-domain features of the signal,and thus causing difficulties in signal recognition.To solve this problem,this paper provides a detailed analysis of the time-domain autocorrelation features of DRM signals.Quantitative analysis is conducted on the relationship between the frame structures,cyclic prefixes,and gain pilots in the DRM signal protocol and the autocorrelation features.The proposed method is verified by the measured data.The proposed time-domain autocorrelation method requires small computational amount and is robust against ionospheric disturbances.The result of this paper can be effectively applied in DRM signal recognition,which is of great significance for radio monitoring in the shortwave broadcasting band.
作者
孙宇
韩琦
张弓
SUN Yu;HAN Qi;ZHANG Gong(Radio Monitoring Station of Heilongjiang Province,Harbin 150001,China;Harbin Institute of Technology,Harbin 150001,China;TP-LINK Technologies Co.,Ltd.,Shenzhen 518000,China)
出处
《自动化与仪器仪表》
2023年第12期89-92,共4页
Automation & Instrumentation
关键词
短波通信
数字调幅广播
时域自相关
特征提取
short wave communication
digital radio mondiale
time domain correlation
feature identification
