基于分数低阶统计量的双谱及其估计方法

Study of Bispectrum Based on Fractional Lower Order Statistics and Its Estimation

稳定分布可更好地描述实际中所遇到的具有显著脉冲特性的随机噪声。为了更好地抑制信号背景中的非高斯噪声,本文提出了基于分数低阶的双谱定义,并给出在分数低阶有色噪声背景下双谱非参数和参数模型的估计方法。仿真结果表明,同传统的双谱估计相比较,非参数法分数低阶双谱估计能有效的识别信号,保留了信号的幅度和相位信息,但存在较大的估计方差。基于AR模型的分数低阶双谱估计具有最大的谱平坦度,能够有效地抑制噪声,具有良好的韧性。

Stable processes can better model the impulsive random signals and noises in physical observation. In order to suppress the non-Gaussian noise, the bispectrum based on fractional lower order statistics is defined, and its estimation method was proposed. Simulation results shows that nonparametric bispectrum based on fractional lower order statistics is much effective for identifying signal compared with traditional bispectrum, and signal amplitude and phase are kept, but it still has big estimated variance. The new algorithm based on the parameter model of AR processes has maximal spectral flatness, and is much robust and effective for suppressing the fractional lower order noise.