MATLAB Implementation Process of FIR Algorithm

The MATLAB implementation process of FIR (Finite Impulse Response) filters typically involves three main stages: design, analysis, and application.

First, FIR filter design is generally based on given filtering requirements, such as passband/stopband frequencies and attenuation specifications. MATLAB provides multiple design methodologies including windowing methods, least-squares approaches, and equiripple techniques. The windowing method is among the most commonly used approaches—it selects appropriate window functions (like Hamming, Hanning, or Kaiser windows) to truncate an ideal filter response, effectively reducing spectral leakage and ripple effects.

Second, MATLAB functions such as `fir1` or `fir2` can generate FIR filter coefficients. The `fir1` function is suitable for standard low-pass, high-pass, band-pass, and band-stop designs, while `fir2` allows for more flexible frequency response shaping. These functions automatically compute optimal coefficients based on specified cutoff frequencies and filter order, ensuring desired filtering performance.

After designing the filter, the `freqz` function can analyze its frequency response, including magnitude and phase characteristics. Due to their inherent linear phase property, FIR filters are widely used in signal processing applications requiring precise phase preservation, such as in communications and audio processing systems.

Finally, the designed FIR filter can be directly applied to signal processing tasks. MATLAB’s `filter` or `conv` functions can perform the actual filtering operation on input signals. Because FIR filters rely solely on input signals (with no feedback loops), they offer superior stability compared to IIR filters, making them suitable for real-time processing and high-precision applications.

MATLAB's optimized matrix operations and efficient algorithms ensure fast implementation of FIR filters, enabling large-scale data processing for both engineering practice and academic research.