Comprehensive Signal Analysis: Unraveling Mean Frequency, Power, and Bandwidth

Objective: The power spectral density (PSD) serves as a fundamental tool in signal analysis, revealing valuable insights into the distribution of power across frequencies. This paper explores the concept of PSD, which emerges from the Fourier transform of a signal’s autocorrelation function. By examining power distribution, PSD provides a comprehensive understanding of a signal’s spectral characteristics. Methods: We generate a chirp signal with specific parameters, including initial and final frequencies, and introduce white Gaussian noise. The combination of these signals forms a composite signal, enabling the calculation of its PSD. Results: Visualizing the PSD allows for discernment of the frequency distribution, with the median frequency providing insights into the central tendency of the distribution. Conclusion: Furthermore, the customization of plots enhances the visual representation of data, tailoring it to the requirements of the specific application. Adjustments to color schemes, line styles, and annotations improve clarity and aid in conveying complex information effectively.