MATEC Web Conf.
Volume 283, 2019The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|Number of page(s)||6|
|Section||Ultrasounds, Signal Processing, and NDT/E|
|Published online||28 June 2019|
Nonparametric and parametric methods of spectral analysis
1 Department of Information Science and Electronic Engineering, Zhejiang University, 310027, Hangzhou, China
2 Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, 316021, Zhoushan, China
* Corresponding author: email@example.com
Spectral Analysis is one of the most important methods in signal processing. In practical application, it is critical to discuss the power spectral density estimation of finite data sampled from some stationary time series. A spectral estimator is expected to have good statistical properties such as consistency, high resolution and small variance. For one spectral estimation method, there exists a trade-off between high resolution and small variance. The paper provides a comparison of several popular spectral methods from both theoretical properties and practical applications. We first address several basic nonparametric methods, whose statistical characters are analysed. Then we explain the connections and differences between temporal windowing and lag windowing. Thereafter, the confidence intervals of both windows are given and used to evaluate the estimated results. Besides, several different parametric estimation methods of autoregressive time series are compared, and whose properties and effects are also introduced. Building on our understanding of these studies, we then apply parametric and nonparametric spectral estimation methods on the data of ocean surface wave height.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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