Design and Application of Band Pass IIR Digital Filters in Ground Cluster in Radar System Using Python

Ground clutter remains a major challenge in radar systems, as low-frequency reflections from stationary objects can obscure high-frequency target signals, reducing detection accuracy. This study addresses this issue by designing and implementing four Infinite Impulse Response (IIR) filters, Chebyshev Type I, Chebyshev Type II, Elliptic, and Bessel, using Python. All filters were developed using the bilinear transformation method under identical specifications to ensure fair comparison. Performance evaluation based on frequency- and time-domain characteristics shows that the Elliptic filter achieved the best overall performance, with 43.5 dB clutter rejection, 94.2% noise reduction, and 9.4 dB target preservation, making it the most effective for clutter suppression. Chebyshev Type I also demonstrated strong attenuation and sharp transition characteristics, while Chebyshev Type II provided moderate suppression with a flat passband. Although offering lower attenuation, the Bessel filter preserved the signal waveform most effectively due to its superior phase linearity. However, the study is limited by the use of simulated signals under controlled conditions, which do not fully capture the complexities of real-world radar, such as dynamic clutter and environmental variability. In addition, implementation was restricted to a single platform. Overall, the results emphasize that filter selection should be application-dependent. Elliptic filters are recommended for maximum clutter suppression, whereas Bessel filters are better suited for phase-sensitive applications. Future work should focus on validating with real radar data, developing adaptive filtering methods, and implementing across multiple platforms to enhance practical applicability.