智能通信中的信号识别与虚警定位仿真  

Orientation of Signal Recognition and False Alarm in Intelligent Communication Simulation

作　　者：徐音[1] 马爱霞[1] 朱旭普 

机构地区：[1]河南理工大学万方科技学院 [2]72749部队

出　　处：《计算机仿真》2016年第9期211-214,共4页Computer Simulation

基　　金：国家自然科学基金(61272490)

摘　　要：在通信信号识别中,为对智能通信中的信号进行识别与虚警定位,提高智能通信的性能,在进行信号识别与定位时,通过瞬时频率,对通信信号类型进行分类后,提取能够区分不同信号类别的关键特征,与已知的信号类型进行比对,完成信号识别与定位。而传统方法进行信号识别和定位时,在针对加性高斯白噪声信道或衰落信道时,容易将瞬时频率信号转变成不易识别的低频信号,导致不能利用瞬时频率对通信信号进行分类,导致信号识别与虚警定位误差大,降低了智能通信性能。提出采用特征提取与小波分类特征的智能通信干扰源信号的识别与定位方法,通过对瞬时频率和复包络模值特征进行统计,实现智能通信信号类型的粗分类,采用连续小波变换和多层小波分解方法提取智能通信信号的特征,通过细微特征完成提取和对比,完成对信号类型的精确识别和定位。仿真结果表明,提出的改进方法节省了对初始信号源变化后的计算,省略了估算计周期,提高了智能通信的设计,操作步骤简单,通信识别的速度和抗干扰强度明显增强。A recognition and location method of interference source signals in intelligent communication is proposed by using feature extraction and wavelet classification feature. Firstly, the rough classification of signal types is achieved according to the statistical analysis of instantaneous frequency and module value of complex envelope. Then, the continuous wavelet transform and the multilevel wavelet decomposition are used to extract the feature of intelligent communication signals. Finally, the extraction and the comparison are completed through subtle feature and the precise recognition and the location of signal types are achieved. The simulation results show that the modified method can save the calculation time of initial signal source after variation and omit the estimation period. It improves the de- sign of intelligent communication and the operation step is easy. It also enhances recognition speed and anti-interference intensity apparently.

关 键 词：智能通信 干扰源 特征提取 小波分类特征 瞬时频率 复包络模值特征 

分 类 号：TP155[自动化与计算机技术—控制理论与控制工程]