Techniques
Techniques for spectrum estimation can generally be divided into parametric and non-parametric methods. The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters (for example, using an auto-regressive or moving average model). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, non-parametric approaches explicitly estimate the covariance or the spectrum of the process without assuming that the process has any particular structure.
Following is a partial list of spectral density estimation techniques:
- Periodogram, a classic non-parametric technique
- Welch's method
- Bartlett's method
- Autoregressive moving average estimation, based on fitting to an ARMA model
- Multitaper
- Maximum entropy spectral estimation
- Least-squares spectral analysis, based on least-squares fitting to known frequencies
Read more about this topic: Spectral Density Estimation
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