Physical Science International Journal

Ground clutter remains a significant challenge in radar systems because low-frequency reflections from stationary objects can mask or distort high-frequency target signals, thereby reducing detection accuracy and overall system performance. This study presents the comparative design, implementation, and application of four Infinite Impulse Response (IIR) digital filters: Chebyshev Type I, Chebyshev Type II, Elliptic, and Bessel using MATLAB and Python for ground-clutter suppression in radar systems. The filters were designed using the bilinear transformation technique under identical specifications and evaluated in both the frequency and time domains to determine their effectiveness in attenuating low-frequency clutter while preserving high-frequency target signals. Simulation results demonstrate that the bilinear transformation method yields stable, accurate digital filters on both platforms. Among the filters, the Elliptic and Chebyshev Type I filters achieved the highest stopband attenuation and sharpest transition bandwidth, making them the most effective for clutter suppression. The Chebyshev Type II filter provided moderate attenuation with a flat passband response, whereas the Bessel filter prioritized phase linearity and waveform preservation at the expense of attenuation strength. MATLAB implementations exhibited slightly sharper roll-off and deeper attenuation due to optimized numerical precision, while Python implementations showed smoother temporal responses and improved transient stability. Comparative analysis revealed only minor differences between the two platforms, mainly due to variations in floating-point precision and coefficient normalization. Overall, both MATLAB and Python proved reliable for digital filter design and implementation. The study concludes that filter selection should depend on the specific radar application, with Elliptic filters preferred for maximum clutter suppression and Bessel filters better suited for phase-sensitive radar systems, thereby supporting efficient and flexible signal-processing solutions.