指定功率谱密度、偏斜度和峭度值下的非高斯随机过程数字模拟

Numerically Simulating Non-gaussian Random Processes with Specified PSD,Skewness and Kurtosis

无记忆非线性变换是传统非高斯随机过程模拟中经常使用的方法,然而,用于模拟同时具有指定功率谱密度、偏斜度和峭度值的非高斯随机过程时迭代过程复杂耗时,且精度也难以保证。通过理论推导,分析并得到了一种新的基于IFFT和时域随机化的非高斯随机过程模拟算法,能够方便快捷地模拟具有指定功率谱密度、偏斜度和峭度值的平稳非高斯信号。在此基础上进行了数值仿真实验,数值仿真结果与理论分析结果相一致,显示了该方法的有效性。

Non-gaussian random process is usually simulated by the method of Zero Memory Nonlinearity（ZMNL）. However, its iterative process is time-consuming and it cannot ensure the precision of the simulation of non-gaussian random processes with specified power spectral density, skewness and kurtosis. Through theoretical derivation, a new algorithm based on IFFT and time domain randomization for the simulation of non-gaussian random processes with specified PSD, skewness and kurtosis was proposed. The numerical experiment is carried out and the results coincide with that from the theory. This shoves that the algorithm is effective.