Measurement and Parameter Estimation of Non-stationary Signals

The article highlights the importance of time-frequency analysis, which serves as a fundamental method for measuring and estimating parameters of non-stationary signals. Time-frequency analysis enables better understanding of the characteristics and behavior of non-stationary signals by revealing their joint time-frequency distributions. Through analytical techniques like Short-Time Fourier Transform (STFT) or Wavelet Transform implementations, we can capture how signal properties evolve simultaneously in both time and frequency domains, thereby extracting comprehensive information about signal dynamics. In practical implementations, algorithms such as spectrogram computation or Wigner-Ville distribution can be programmed using signal processing libraries to visualize time-frequency representations. Consequently, time-frequency analysis finds extensive applications in signal processing and communication fields, providing effective solutions to numerous real-world problems including signal detection, feature extraction, and system identification.