采用因式分解提高功率谱密度参数估计的分辨能力

IF 0.2 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Visnyk NTUU KPI Seriia-Radiotekhnika Radioaparatobuduvannia Pub Date : 2023-03-28 DOI:10.30837/rt.2023.1.212.08
V.A. Tikhonov, V.M. Bezruk
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引用次数: 0

摘要

我们考虑了基于自回归线性预测模型的随机过程参数谱功率密度(PSM)估计的因式分解的可能性,以提高谱分辨率。因数分解是指将多模PSM分解成更简单的单模组件。因式分解不仅可以将复杂的多模PSM分解为简单的单模分量,而且可以更准确地分析随机过程中SPM的低、中、高频分量。重点研究了利用Yule-Walker和Berg方法对SPM估计进行分解以提高分辨能力的问题。
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Applying factorization to increase the resolving ability of the parametric estimation of the power spectral density
We consider a possibility of the factorization of parametric spectral power density (PSM) estimation of a random process based on autoregressive linear prediction model to increase the spectrum resolution. Factorization refers to the decomposition of the multimode PSM into simpler single-mode components. Factorization makes it possible not only to decompose a complex multimode PSM into simple single-mode components, but also to analyze more accurately the low-, medium- and high-frequency components of the SPM of a random process. The main attention is paid to the study of the problem of increasing the resolving power of SPM estimation by its factorization by the Yule-Walker and Berg method.
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Visnyk NTUU KPI Seriia-Radiotekhnika Radioaparatobuduvannia
Visnyk NTUU KPI Seriia-Radiotekhnika Radioaparatobuduvannia ENGINEERING, ELECTRICAL & ELECTRONIC-
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