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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FAQ: Lomb-Scargle periodogram for complex exponential signal

What is a Lomb-Scargle periodogram?

A Lomb-Scargle periodogram is a method used in signal processing to identify periodic signals in unevenly spaced data. It is particularly useful in astronomy and other fields where data sampling may not be uniform. The method extends the traditional Fourier transform to handle such irregular sampling effectively.

How does the Lomb-Scargle periodogram handle complex exponential signals?

The Lomb-Scargle periodogram can be adapted to handle complex exponential signals by extending the algorithm to work with complex-valued data. This involves treating the real and imaginary parts separately and combining the results to detect periodicities in the complex signal.

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

The primary advantage of the Lomb-Scargle periodogram is its ability to handle unevenly spaced data without the need for interpolation. This makes it more robust and accurate for many real-world applications where data may not be uniformly sampled. Additionally, it can provide more precise frequency estimates for such data.

What are the key parameters in a Lomb-Scargle periodogram analysis?

The key parameters in a Lomb-Scargle periodogram analysis include the frequency range to be analyzed, the number of frequency bins, and the normalization method. These parameters determine the resolution and accuracy of the periodogram and must be chosen based on the specific characteristics of the data and the desired outcomes of the analysis.

Can the Lomb-Scargle periodogram be used for real-time signal processing?

While the Lomb-Scargle periodogram is typically used for offline analysis of collected data, it can be adapted for real-time signal processing. However, this requires efficient computation methods and may involve trade-offs in terms of resolution and computational complexity. Real-time applications would benefit from optimized algorithms and hardware acceleration.

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