摘要
1.引言 在时间序列分析中,多数文献讨论的是线性高斯模型,在高斯假设下,最小二乘估计就是最大似然估计;最小二乘估计只利用了时间序列的自相关信息,所形成的方程是一组线性方程即Wiener-Hopf方程。因此,最小二乘估计被广泛地应用于时间序列的建模。但是,自相关序列是“相盲”的,相关建模方法不能准确地表征非最小相位的参数信号;在自相关(功率谱)域中,所能做到的只是重建功率谱意义下等效的最小相位信号。
Causal AR models can only provide minimum-phase information accurately whereas anti-causal AR models provide maximum-phase information . The parameters of causal and anti-causal AR models are estimated by using third-order cumulant sequences on the same data and properly utilized to obtain non-minimum phase function of signals. The estimated non-minimum phase function can be used as reference-phase function to resolve the true location of signal zeros by minimum square error criterion. The results of computer simulations are included to illustrate the conclusions in the paper.
出处
《航空学报》
EI
CAS
CSCD
北大核心
1991年第7期A431-A434,共4页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金
关键词
相位估计
非最小相位
时间序列
phase estimation, non-minimum phase, higher-order statistics, causal AR model, anti-causal AR model.