Window Type & Length Impact on Frequency Leakage in DFT Spectrum Analysis

Digital Frequency Spectrum Analysis based on DFT (Discrete Fourier Transform) is a widely used method for analyzing the frequency components of a signal. In this method, the signal is divided into a series of short segments, and each segment is multiplied by a window function before applying the DFT algorithm. The window function helps to reduce the spectral leakage caused by the finite length of the signal segment.

The window type and the window length are two important parameters that affect the frequency leakage in DFT-based spectrum analysis. The window type determines the shape of the window function and how it affects the signal segment. The most commonly used window types are 'rectangular', 'Hanning', 'Hamming', and 'Blackman'.

The 'rectangular' window is the simplest window type and has a constant value of 1 over the entire segment. This window type does not provide any attenuation of the signal at the edges of the segment, leading to high-frequency leakage.

The 'Hanning' and 'Hamming' windows are designed to reduce the spectral leakage by tapering the signal segment at the edges. The 'Hanning' window has a smoother taper compared to the 'Hamming' window. These windows provide better frequency resolution than the 'rectangular' window but at the cost of reduced amplitude accuracy.

The 'Blackman' window is designed to provide even better attenuation of the spectral leakage than the 'Hanning' and 'Hamming' windows. It has a more complex shape with three terms and provides the best frequency resolution and amplitude accuracy.

The window length determines the duration of the signal segment. A longer window length provides better frequency resolution but at the cost of reduced time resolution. A shorter window length provides better time resolution but at the cost of reduced frequency resolution.

In summary, the choice of window type and window length affects the frequency leakage in DFT-based spectrum analysis. The window type determines the shape of the window function and how it affects the signal segment, while the window length determines the balance between time and frequency resolution. A careful selection of these parameters is necessary to obtain accurate and meaningful frequency analysis results.