MATLAB Implementation of FIR Filters with Rectangular and Hamming Windows

We can implement FIR (Finite Impulse Response) filters using MATLAB, offering two primary window function options: rectangular window and Hamming window. These window functions play a crucial role in signal processing by providing different filtering characteristics and frequency response behaviors. In MATLAB filter design, we typically use the fir1 function to create window-based FIR filters. The rectangular window (also known as boxcar window) provides the sharpest transition band but results in higher sidelobe levels, while the Hamming window offers better sidelobe suppression at the cost of a wider transition band. For code implementation: - Use 'rectwin' for rectangular window implementation: b = fir1(n, Wn, 'rectwin') - Use 'hamming' for Hamming window implementation: b = fir1(n, Wn, 'hamming') where 'n' represents the filter order and 'Wn' specifies the normalized cutoff frequency. The choice between window functions depends on specific application requirements. Rectangular windows are suitable when needing sharp frequency cutoffs, while Hamming windows are preferred for applications requiring reduced ripple and better stopband attenuation. MATLAB's Filter Design and Analysis tool (filterDesigner) also provides interactive ways to visualize and compare these windowing techniques. When designing filters in MATLAB, we can evaluate performance using frequency response analysis (freqz), impulse response examination, and practical signal filtering tests to ensure the desired filtering outcomes are achieved.