Modulation domain provides insight to complex modulated signals and opens a new field of measurement technology.
调制域为人们打开了分析复杂调制信号的新视角,开辟了测量技术的新天地。
In this research, based on statistical pattern recognition method, we use constellation of modulated signals to classify modulation modes both in non-cooperative and cooperative communications.
本文采用基于统计模式识别的方法,研究了非合作通信与合作通信中通过提取调制信号的星座图特征对调制模式进行识别的问题。
Three features parameters derived from the instantaneous amplitude, instantaneous phase, instantaneous frequency and power spectrum of analogue modulated signals are presented in this article.
首先介绍从信号幅度、相位、频率及功率谱等特性中提取的三种特征参数,应用这三种参数采用人工神经网络对模拟调制信号进行了识别。
Frequency of modulated signals is extracted from frequency-modulated signals by using the relationship between the modulus of wavelet transform coefficient and the singular exponent.
利用小波变换系数的模值与信号奇异性指数之间的关系,从调频信号中提取出调制信号的频率。
Different types of digitally modulated signals that have similar, if not identical, power spectral density functions can have highly distinct spectral correlation functions.
一些不同的数字调制信号有着相同或相近的功率谱密度,但它们的谱相关函数却有明显区别。