2. Principle

STFT operates by applying a window function to the signal and computing the Fourier Transform within each windowed segment.

The window is typically overlapped as it slides over time, allowing continuous tracking of frequency changes and allowing continuous tracking of frequency changes and improving temporal continuity

– Illustration: windowing and overlapping sliding process

3. Representation

The result of STFT is represented as a spectrogram, which shows how the magnitude of frequency components varies over time.

Although phase information is also included in the STFT result, it is typically not visualized in basic spectrogram representations.

-. Illustration: spectrogram (time vs frequency vs magnitude)

4. Interpretation

In the spectrogram, high-intensity regions indicate dominant frequency components at specific times.

The distribution over time reveals how signal characteristics change, including transient events and time-varying patterns.

– Illustration: time-varying frequency example

5. Radar Application

Compared to FFT, STFT is more effective in detecting micro-Doppler effects and transient events by capturing how frequency components change over time.

This includes detailed motion patterns such as human walking (arm and leg movements), rotating objects like propellers or fans,

and short-duration target behaviors that appear as time-varying frequency signatures.