计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (20): 178-182.

• 信息与信号处理 • 上一篇    下一篇

分数低阶时频滑动平均模型参数估计

汪海滨1,龙俊波2,查代奉3   

  1. 1.九江学院 信息科学与技术学院,江西 九江 332005
    2.九江学院 电子工程学院,江西 九江 332005
    3.九江学院 理学院,江西 九江 332005
  • 出版日期:2015-10-15 发布日期:2015-10-30

Modeling and parameter estimation based on FLO-TFMA

WANG Haibin1, LONG Junbo2, ZHA Daifeng3   

  1. 1.College of Information Science and Technology, Jiujiang University, Jiujiang, Jiangxi 332005, China
    2.College of Electronic Engineering, Jiujiang University, Jiujiang, Jiangxi 332005, China
    3.College of Science, Jiujiang University, Jiujiang, Jiangxi 332005, China
  • Online:2015-10-15 Published:2015-10-30

摘要: 针对稳定分布环境下非平稳过程分析方法时频滑动平均(TFMA)模型算法的退化,引入分数低阶统计量共变,提出了一种改进的分数低阶时频时频滑动平均(FLO-TFMA)模型算法。推导了FLO-TFMA模型的参数求解过程,给出了基于FLO-TFMA模型的时频谱估计。通过在稳定分布环境下对TFMA模型算法和所提出的FLO-TFMA模型算法的参数估计均方误差(MSE)比较和时频谱估计比较,仿真结果表明,FLO-TFMA模型算法的参数估计精度优于TFMA模型算法,TFMA模型时频谱估计完全失效,而FLO-TFMA模型时频谱算法能较好地进行时频谱估计。

关键词: 稳定分布, 分数低阶统计量, 滑动平均模型, 非平稳过程, 时频谱估计

Abstract: The Time-Frequency Moving Average (TFMA) model algorithm which is a method of non-stationary signal processing  degenerate under [α] stable distribution environment, the fractional lower order statistics covariance  is introduced and the improved Fractional Lower Order Time-Frequency Moving Average algorithm (FLO-TFMA) model algorithm is proposed. The parameters estimation of FLO-TFMA model is developed and time-frequency spectrum estimation is given based on the FLO-TFMA model. By comparing the Mean Square Error (MSE) of parameter estimation and spectrum estimation of the TFMA model algorithm and the proposed FLO-TFMA model algorithm under [α] stable distribution environment condition, simulations show that the parameters estimation precision of the FLO-TFMA model algorithm is better than TFMA model algorithm, the TFMA model spectrum estimation can not work, and FLO-TFMA model algorithm provides better performance of time-frequency spectrum.

Key words: [α] stable distribution, fractional lower order statistic, moving average model, non-stationary process, time-frequency spectrum estimation