MA parameter estimation and cumulant enhancement
MA parameter estimation and cumulant enhancement
复制标题
MA参数估计和累积量增强
DOI：
10.1109/78.510618
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
S. McLaughlin
中科院分区：
文献类型：
--
作者：
A. G. Stogioglou;S. McLaughlin
本文讨论的问题估计的移动平均（MA）模型的参数，无论是从三阶或四阶累积量的噪声观测系统输出。该系统是由一个独立的和同分布的非高斯序列，是不可观察的。未知的模型参数，使用批处理最小二乘法获得。递归方法也被开发和使用来要求批处理最小二乘解的唯一性。介绍了一种新的MA过程三阶累积量增强技术。这种新技术是基于复合性质映射的概念，有助于减少MA过程的三阶（或四阶）累积量估计的方差。仿真结果证明了新方法的性能，并将其与现有技术的范围进行比较。
This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods are also developed and used to claim the uniqueness of the batch least squares solutions. A novel technique for the enhancement of third-order cumulants of MA processes is introduced. This new technique is based on the concept of composite property mappings and helps reduce the variance of the estimates of third- (or fourth)-order cumulants of MA processes. Simulation results are presented that demonstrate the performance of the new methods and compare them with a range of existing techniques.