某些ARMA模型在功率谱密度估计中的性能评价

T. Srinivasan
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引用次数: 1

摘要

对于自回归移动平均(p, q)过程,考虑的性能指标是谱估计量的渐近方差和两个紧密间隔的正弦波在白噪声中的分辨率。虽然AR参数是获得良好分辨率的主要原因,但在某些方法中,适当的MA参数也是必要的。比较了Cadzow(间接)方法和奇异值分解(SVD)方法。结果表明,两种方法在目标频率附近的PSD估计方差近似相同。SVD方法产生的模型阶数比Cadzow方法低得多,其中MA参数对分辨率的影响更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance evaluation of certain ARMA models in power spectral density estimation
For an autoregressive moving average (p, q) process the performance measures considered are the asymptotic variance of the spectral estimator and the resolution of two closely spaced sinusoids in white noise. Though the AR parameters are mainly responsible for good resolution, it is shown that proper MA parameters are also necessary in some methods. Cadzow's (Indirect) method and the singular value decomposition (SVD) method are considered for comparison. It is found that both methods have approximately the same variance of the PSD estimates in the neighborhood of the frequencies of interest. The SVD method yields a much lower model order than Cadzow's method in which the MA parameters have greater influence on resolution.<>
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