基于联合特征参数提取的非合作信号调制识别算法

Non-cooperative signal modulation recognition algorithm based on joint feature parameter extraction

针对当前电磁环境中调制方式复杂,用频设备增多,频谱拥塞且电磁环境干扰增加的情况,研究了以非合作通信为背景的OFDM信号检测与子载波识别算法。利用OFDM信号与单载波信号在时域上的不同分布状态提出一种利用联合特征参数解决接收信号中OFDM的存在性问题;针对信道传输给信号带来的相移和频偏问题,根据周期平稳性进行参数盲估计得到信号先验信息;在已获取信号先验信息的基础上,提出一种非合作OFDM信号子载波信号多层级分类识别的方法。由此设计了基于非合作通信系统OFDM信号检测与子载波调制识别的模型,最后完成了未知信号的调制识别。仿真实验表明,在非合作通信系统中能准确识别OFDM信号与单载波信号,能对接收方OFDM信号子载波中的空子载波、QPSK和16QAM达到理想的调制识别效果,克服了信道传输带来的相移和频偏问题,提高了调制方式识别的准确率。

Aiming at the problem that in the current electromagnetic environment,the modulation method is complicated,the frequency-consuming equipment increases,the spectrum is congested,and the electromagnetic environment interference increases,the algorithm of OFDM signal detection and subcarrier identification in the background of non-cooperative communication were deeply studied.Using the different distribution states of OFDM signals and single carrier signals in the time domain,a joint characteristic parameter was proposed to solve the existence problem of OFDM in the received signal.For the phase shift and frequency offset problems caused by the channel transmission to the signal,by using the periodic stability the blind parameter estimation was performed to obtain the signal prior information.On the basis of the obtained signal prior information,a multi-level classification and recognition method for non-cooperative OFDM signal sub-carrier signals was proposed.Therefore,a model based on non-cooperative communication system OFDM signal detection and subcarrier modulation identification was designed,and finally modulation identification of unknown signals was completed.Simulation experiments show that in non-cooperative communication systems,OFDM signals and single-carrier signals can be accurately identified,and ideal modulation recognition effects can be achieved on empty subcarriers,QPSK,and 16QAM in the receiver OFDM signal subcarriers,overcoming the channel transmission band The problems of phase shift and frequency offset have improved the accuracy of modulation mode identification.