Spectrum Analysis Implementation Using FFT

Recently I noticed someone asking how to implement spectrum analysis using FFT. I believe those who have progressed to spectrum analysis have already achieved considerable depth in system research. If you haven't performed spectrum analysis yet, this is an excellent starting point for learning. For beginners in spectrum analysis, I've created a practical example routine for reference. The main components of the package (signal_analyzer.rar) are as follows:

- test1.txt: The first column represents time t with a 0.001s step size; the second column contains the system's input signal; the third column contains the system's output signal. The MATLAB code uses 'readmatrix' or 'load' function to import this time-series data into the workspace for processing.

- signal_analyzer.m: This script first imports test1.txt data into the workspace, then performs FFT analysis to determine both signals' frequency characteristics. The implementation uses MATLAB's built-in fft function with proper windowing and zero-padding for accurate frequency resolution. The algorithm calculates magnitude spectra by taking the absolute value of FFT results and normalizing by the number of samples.

- Amplitude attenuation (dB) between output and input signals: This calculation involves converting magnitude ratios to decibel scale using 20*log10(output_magnitude/input_magnitude). Understanding amplitude attenuation is crucial for analyzing system gain characteristics and frequency response optimization.

- Phase difference between output and input: Computed using angle(fft_output) - angle(fft_input), then unwrapped to avoid phase wrapping artifacts. Phase difference analysis helps evaluate system stability, response speed, and phase margin in control systems.

I hope this example proves helpful for your learning journey. I encourage you to delve deeper into spectrum analysis and FFT fundamentals to enhance both the depth and breadth of your system research capabilities.