Short-Time Fourier Transform Analysis of Linear Frequency Modulated Signals

Short-Time Fourier Transform (STFT) analysis of Linear Frequency Modulated (LFM) signals represents a fundamental signal processing methodology. By applying STFT to LFM signals, researchers can obtain comprehensive time-frequency analysis results that reveal signal characteristics across both temporal and spectral domains. The STFT algorithm operates by segmenting the input signal into overlapping time windows using functions like `hann()` or `hamming()` for windowing, then performing Fast Fourier Transform (FFT) on each windowed segment using computational methods such as `fft()` in MATLAB or Python's `scipy.fftpack.fft`. This process generates spectral information for each temporal segment, which when aggregated through techniques like spectrogram plotting (`spectrogram()` in MATLAB or `matplotlib.pyplot.specgram` in Python), produces a complete time-frequency representation. This visualization effectively uncovers the signal's evolutionary patterns across different frequencies and time instances, making STFT particularly valuable for applications in radar signal processing, communications systems, and biomedical signal analysis where LFM signals are prevalent. The method's implementation typically involves parameter optimization including window size selection, overlap percentage, and frequency resolution adjustments to balance time-frequency localization precision.