Measuring Power Spectral Analysis in dBvolts vs Frequency: Tips and Techniques

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SUMMARY

This discussion focuses on measuring the area under the curve in power spectral analysis expressed in dBvolts versus frequency (Hz). The key technique involves using the Full Width at Half Maximum (FWHM) to establish a relationship between the spectral peak and the area under the curve. While analytical methods using Gaussian or Lorentzian functions can provide insights, they may lack accuracy unless the spectrum closely matches the chosen model. A more reliable approach is to numerically integrate the actual spectrum for precise area calculations.

PREREQUISITES
  • Understanding of power spectral analysis concepts
  • Familiarity with Full Width at Half Maximum (FWHM) measurement
  • Knowledge of Gaussian and Lorentzian function modeling
  • Experience with numerical integration techniques
NEXT STEPS
  • Explore numerical integration methods for spectral data analysis
  • Learn about fitting techniques for Gaussian and Lorentzian functions
  • Investigate the implications of FWHM in spectral analysis
  • Study advanced power spectral analysis tools and software
USEFUL FOR

Researchers, data analysts, and engineers involved in signal processing and spectral analysis who seek to accurately measure and interpret frequency domain data.

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when looking at a power spectral analysis dBvolts vs frequency (Hz), how can you measure the area under the curve of a specific frequency range using the width at half height? any ideas?
 
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If you represent the spectral peak as some functional form (Gaussian, Loretnz, etc.), you could probably analytically (or semi-numerically) work out a connection between FWHM and area under curve for a specified frequency range. This wold be true if the parameterization fixed the shape of the curve given just the parameter FWHM. Then, the area is obtained by integrating this curve over desired frequency range. Of course, this will not be very accurate in general (unless your spectrum is well modeled by the analytical form). Else, you could use a sum of Gaussians, and do a fit. Easier to just numerically integrate your actual spectrum.
 

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