Sliding a window by the hop length and partitioning input signal into window length interval.

 

 

 

 

 

2. Principle

The partitioned input signals typically overlap by the difference between the window and hop sizes.

This overlap ensures temporal continuity and seamless frequency tracking.

Following the application of a window function to each segment, a FFT is proceeded to map the signal into the time-frequency domain.

 

 

Window Function(HANN, Hamming, or etc…,)

 

 

FFT after applying window function on each time-segmented input signal → Time-Frequency Spectrum

 

 

 

 

 

 

3. Representation

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

The color intensity represents the energy level at each time and frequency.

Unlike a standard FFT, the spectrogram includes a time axis where each point corresponds to the individual time-segmented intervals.

 

 

Spectrum at each time		Spectrogram
		The intensity is the energy level at each time and  frequency.

 

 

 

 

4. Interpretation

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

Repeating patterns over time indicate periodic behavior,

while vertical spreading reflects transient events and short-duration changes in motion.

 

 

 

 

 

 

5. Radar Application

While a standard FFT analyzes the signal as a whole and loses time information,

STFT effectively detects micro-Doppler effects and transient events by capturing time-varying frequency signatures.

As a result, it can identify complex motion patterns like human gait, rotating objects, and rapid target behavior