2. Principle

Fourier transform operates by taking the inner product of the input signal with sinusoidal basis functions at different frequencies.

This process separates the signal into its constituent frequency components, allowing each frequency component to be quantified.

The FFT is an efficient algorithm used to compute this transformation.

 

 

 

 

 

 

 

3. Representation and Interpretation

The FFT result allows easy identification of the frequency components that constitute an arbitrary time-domain signal.

Although phase information is also part of the FFT result, it is often not visualized in basic spectrum representations.

 

 

In the spectrum, peaks indicate dominant frequency components present in the signal.

The amplitude of each peak represents the signal strength at that frequency, while the overall distribution reveals signal characteristics and noise.

 

 

 

 

 

4. Radar Application

In radar systems, FFT is used to extract key physical information such as range, velocity, and angle.

Range FFT converts time-domain signals into distance, while Doppler FFT estimates velocity from frequency shifts.

As a fundamental and efficient processing tool, FFT forms the basis of most radar systems, with advanced methods used only in specific cases.