非最小相位ARMA模型的一种自适应辨识算法
AN ADAPTIVE IDENTIFICATION ALGORITHM FOR NONMINIMUM PHASE ARMA MODELS
-
摘要: 本文提出了一种加性有色高斯噪声中因果非最小相位ARMA模型的自适应辨识算法。模型输入假定为非高斯独立同分布随机过程。算法只利用了观测信号的高阶统计量。在每次迭代中,先估计AR参数,再估计MA参数,但不用计算残差序列。在参数递推中采用了随机梯度法。仿真实验证实了本文算法的有效性。Abstract: This paper proposes an adaptive identification algorithm for nonminimum phase ARMA models in additive colored Gaussian noise. The model input is assumed to be an i. i. d., non-Gaussian random process. The algorithm utilizes higher-order statistics of the observed signal alone. It estimates the AR and MA parameters successively in each iteration without computing the residual time series. The stochastic gradient method is used in parameter updating. Simulation resutls show the effectiveness of the algorithm.
-
Nikias C L, Mendel J M. Signal processing with higher-order spectra[J].IEEE Signal Processing Magazine.1993, 10(3):10-37[2]Mendel J M. Tutorial on higher-order statistics (spectra) in signal processing and system theory:[3]Theoretical results and some applications. Proc[J].IEEE.1991, 79(3):278-305[4]张贤达.现代信号处理.北京:清华大学出版社,1995.[5]Friedlander B, Porat B. Adaptive IIR algorithms based on higher-order statistics. IEEE Trans. on ASSP, 1989, ASSP-37(4): 485-495.[6]Giannakis G B. On the identifiability of non-Gaussian ARMA models using cumulants. IEEE Trans. on AC, 1990, AC-35(1): 18-35.[7]Rosenblatt M, Van Ness J N. Estination of the bispectrum[J].Ann. Math. Stat.1965, 36:1120-1136[8]Arnold S F. Mathematical Statistics. Englewood Cliffs, NJ: Prentice-Hall, 1990, Sec. 7.2.2.[9]Zhang X D, Zhang Y S. FIR system identification using higher-order statistics alone. IEEE Trans. on SP, 1994, SP-42(10): 2854-2858.[10]Luenberger D G. Linear and Nonlinear Programming. 2nd ed., Reading, MA: Addison-Wesley, 1984, Sec. 7.6. -
计量
- 文章访问数: 2033
- HTML全文浏览量: 116
- PDF下载量: 419
- 被引次数: 0
下载:
