Cyclostationary Signal Analysis Toolkit

This article introduces the fundamental concepts of cyclostationary signal analysis tools and provides an in-depth exploration of two core components: autocorrelation functions and experimental demonstrations. The autocorrelation function serves as a mathematical tool for analyzing temporal relationships within signals across different time points. By computing signal correlations at various time lags, we can uncover temporal variation patterns and identify potential periodic components through algorithms that typically implement time-domain averaging or Fourier-based approaches using functions like xcorr() in MATLAB or numpy.correlate() in Python.

We present seven structured experiments designed to enhance understanding of cyclostationary signal characteristics and behaviors. These experiments employ diverse algorithmic methods including spectral correlation density estimation, cyclic periodogram analysis, and frequency-domain smoothing techniques. Each experiment systematically investigates signal periodicity, frequency components, and phase relationships through practical implementations that may involve FFT-based computations, wavelet transforms, or statistical hypothesis testing. Through these hands-on investigations, we establish a comprehensive framework for applying cyclostationary signal analysis tools in real-world scenarios, demonstrating their significance in fields ranging from communications engineering to mechanical vibration analysis.