Lomb-Scargle periodogram for complex exponential signal

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tworitdash
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I found a paper by Brethorst where he developed a periodogram that is a generalized version of the Lomb-Scargle periodogram. You can find it here [1].

I tried to implement (22) from this paper to make a periodogram for an aperiodically sampled complex data that is stochastic. I observed that it is the same as a Schuster periodogram. I want to verify what I did. Please let me know if something is wrong.

In the paper, they added a decay factor in the model ## Z ##, which I set to [itex ] 0 [/itex ].
Second, they also have different lengths for the real and imaginary parts of the signal. However, for me, they are collected at the same time. ## N_R = N_I = N_d ##, and ## t_i = t_j ##.

I choose the $H$ as the basis ## H = 2 \pi f t ## as they do in (23).

If I go by these assumptions, the following quantities become:

$$\theta = \frac{1}{2} \tan^{-1}\left(\frac{0}{0}\right) = 0$$ From (20)
$$ C = N_d $$ from (17)
$$ S = N_d $$ from (18)

$$ R = \sum_{i = 1}^{N_d} d_R(t_i) \cos{(H(t_i))} - d_I(t_i) \sin{(H(t_i))} $$
$$ I = \sum_{i = 1}^{N_d} d_R(t_i) \sin{(H(t_i))} + d_I(t_i) \cos{(H(t_i))} $$

Here, ## d_R = \Re({z}) ##, and ## d_I = \Im({z}) ##. So, the final expression (22) becomes:

$$ \bar{h}^2 = \frac{1}{N_d} \times (R^2 + I^2) $$

I think this is the same as the Schuster periodogram. Am I correct? In that case, which periodogram should I use with lower side-lobe levels than the Schuster periodogram for the aperiodically sampled complex signal?

[1]: https://bayes.wustl.edu/glb/general.pdf
 
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What is a Lomb-Scargle periodogram and how is it used with complex exponential signals?

The Lomb-Scargle periodogram is a method used in signal processing to detect and characterize periodic signals in unevenly-sampled data. When dealing with complex exponential signals, which can represent oscillations in both amplitude and phase, the Lomb-Scargle periodogram is particularly useful. It extends the Fourier analysis to datasets with gaps and irregular sampling intervals, allowing for the estimation of the power spectrum of the underlying periodic components.

How does the Lomb-Scargle periodogram handle non-uniformly sampled data?

The Lomb-Scargle periodogram is specifically designed to handle non-uniformly sampled data by fitting sinusoidal models to the observed data points without the need for interpolation or resampling, which can introduce errors or biases. It computes the periodogram using the least squares fitting of sine and cosine functions, effectively estimating the power of different frequencies present in the dataset despite the irregular sampling.

What are the advantages of using the Lomb-Scargle periodogram over traditional Fourier Transform methods?

The primary advantage of the Lomb-Scargle periodogram over traditional Fourier Transform methods is its ability to accurately analyze data with gaps or irregular sampling intervals. Traditional Fourier methods typically require evenly spaced data and might need preprocessing steps like interpolation, which can distort the signal. The Lomb-Scargle periodogram avoids these issues, providing more reliable frequency analysis in such cases. Additionally, it can directly provide the significance of detected frequencies through statistical methods, which is less straightforward in traditional Fourier analysis.

Can the Lomb-Scargle periodogram be applied to any type of time series data?

While the Lomb-Scargle periodogram is versatile and can be applied to a wide range of time series data, its effectiveness depends on the nature of the data and the specific frequencies being investigated. It is most effective for data where the signal is periodic or quasi-periodic and can be modeled well by sinusoidal functions. However, for highly noisy data, non-periodic data, or data with complex non-sinusoidal shapes, other methods might be more appropriate.

What are the limitations of the Lomb-Scargle periodogram?

Despite its strengths, the Lomb-Scargle periodogram has several limitations. One major limitation is its assumption that the underlying periodic signals can be adequately modeled using sinusoids, which might not hold true for all types of signals, particularly those with sharp features or non-sinusoidal waveforms. Additionally, while it handles gaps in data well, very large gaps or extremely sparse data can reduce the reliability of the frequency estimates. Furthermore, like any statistical tool, it requires careful interpretation and validation of results, particularly in distinguishing true signals from noise.

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