MATLAB Implementation of Spectral Analysis for Random Signals

Spectral analysis of random signals is a MATLAB-based methodology for conducting frequency-domain analysis of stochastic signals. In spectral analysis, we utilize power spectrum (auto-spectrum) and cross-spectrum techniques to characterize signal frequency properties. Window functions such as Hamming, Hanning, or Blackman are essential tools for reducing spectral leakage during analysis. MATLAB implementation typically involves using functions like pwelch() for power spectral density estimation, which applies Welch's method with customizable segment overlap and windowing. The cpsd() function is employed for cross-spectral density computation between two signals. Through spectral analysis of random signals, engineers can identify dominant frequency components, detect periodicities, and analyze signal interactions, making this technique crucial for applications in vibration analysis, communications, and signal processing research. Mastering spectral analysis methods with MATLAB provides practical advantages for engineers and researchers working with real-world stochastic data.