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

Keywords: Bilinear Transformation; Ground Clutter Suppression; IIR digital filters; Radar Signal Processing;

Python.

Radar systems play a critical role in modern surveillance, navigation, meteorology, and defence applications by

enabling the detection and tracking of targets in complex environments. However, one of the major challenges

affecting radar performance is ground clutter, arising from reflections from terrain, buildings, vegetation, and

other stationary objects. These unwanted echoes typically occupy the low-frequency spectrum and can

significantly degrade the detection of moving or airborne targets by masking useful signals and reducing signal-

to-noise ratio (SNR) (Skolnik, 2020; Richards et al., 2022). Effective suppression of ground clutter is therefore

essential for improving radar accuracy and reliability.

Several classical IIR filter families including Chebyshev Type I, Chebyshev Type II, Elliptic, and Bessel filters

offer distinct trade-offs between magnitude response, phase characteristics, and transition sharpness. Chebyshev

Type I filters provide rapid roll-off at the expense of passband ripple, while Chebyshev Type II filters maintain

a flat passband with ripple confined to the stopband. Elliptic filters achieve the steepest transition for a given

order but introduce ripple in both bands, whereas Bessel filters prioritize linear phase response and waveform

preservation over sharp attenuation (Mitra, 2002; Wanhammar, 2020). Selecting the most appropriate filter for

radar applications therefore depends on balancing clutter rejection efficiency against signal distortion and phase

linearity.

The design of digital filters from analogue prototypes is commonly achieved using transformation techniques,

among which the bilinear transformation method remains one of the most widely adopted due to its stability

This study therefore focuses on the design and implementation of IIR digital filters for ground-clutter

suppression in radar systems using Python. Specifically, it investigates the performance of Chebyshev Type I,

Chebyshev Type II, Elliptic, and Bessel filters in attenuating low-frequency clutter while preserving target

signals. By analyzing both frequency-domain and time-domain characteristics, as well as practical application

outcomes, the study provides insight into the strengths and limitations of each filter type and implementation

platform. The findings aim to guide researchers and engineers in selecting appropriate filtering techniques and

computational tools for efficient radar signal processing.

The methodology adopted in this study consists of signal generation, filter design, software implementation, and

performance evaluation.

Four IIR high-pass filters Chebyshev Type I, Chebyshev Type II, Elliptic, and Bessel were designed using

identical specifications to ensure comparability. The passband edge frequency was set to 200 Hz, while the

stopband edge frequency was fixed at 600 Hz. A passband ripple of 1 dB and a minimum stopband attenuation

of 60 dB were specified for the ripple-based filters. The filter order was fixed at nine for all designs. These

parameters were selected to represent a practical compromise between noise suppression effectiveness and

computational efficiency.

Each of the filter families employed in this study originated from a normalized analog prototype with a known

s-domain transfer function, H(s). The prototype was defined as:

Chebyshev Type I: Exhibits equiripple behavior in the passband and a monotonic stopband. The analogue

ripple factor, n is the filter order, and Tn(Ω) is the angular frequency.

(3)

Where H(s) is the transfer function of the analog filter, s is the complex frequency variable, θn(s/ω) is the Filter-

defining function, θn(0) is the normalization constant, ω is the cut-off angular frequency, and n is the filter order.

Elliptic (Cauer)

The magnitude response of an nth-order elliptic (Cauer) filter is defined by the following expression (Diab &

Mahmoud, 2020):

is the passband ripple factor, Rn(Ω,k) is the Jacobian elliptic rational function (Elliptic function of order), k

elliptic modulus (shape factor), and n is the filter order.

the same design specifications, and the noisy signal was processed accordingly. NumPy was used for numerical

computations,

Performance Evaluation Metrics

Filter performance was evaluated using both qualitative and quantitative measures. Qualitative assessment

involved visual inspection of time-domain signals and frequency spectra before and after filtering. The

quantitative evaluation was based on the signal-to-noise ratio (SNR) and the mean square error (MSE). Input

and output SNR values were computed to determine the SNR gain, while the MSE before and after filtering

quantified the effectiveness of noise reduction. These metrics provided an objective basis for comparing filter

performance across designs and platforms.

front-end, where it is amplified and frequency-converted. After analogue-to-digital conversion, digital signal

processing techniques are applied. A high-pass IIR filter is used to suppress low-frequency ground clutter,

followed by detection and tracking stages to extract target information for display

Effective suppression of these low-frequency components is essential for accurate detection of moving or

airborne targets. This section presents a comparative analysis of four classical high-pass digital filters:

Chebyshev Type I, Chebyshev Type II, Elliptic, and Bessel, implemented Python, with an emphasis on their

ability to reject ground clutter while maintaining target signal integrity.

The filters were designed with the following parameters:

Fs = 2000 Hz, Fp = 200 Hz, Fsb = 600 Hz, Rp = 1 dB, Rs = 60 dB, and filter order N = 9

These values were chosen to ensure steep high-pass transitions above the clutter-dominant region (<800 Hz)

while maintaining a stable passband for target echoes.

Fig 2. Radar Application High Pass (PYTHON)

Chebyshev Type I and Elliptic filters maintained comparable attenuation (~65–75 dB) below 800 Hz, with

slightly smoother transitions that reduce transient ringing in the time domain. This smoother response is

advantageous for real-time radar processing, where continuous clutter adaptation is required.

slightly weaker near the cut-off frequency than MATLAB results.

The Bessel Filter preserved the cleanest phase and step response among all filters, minimizing group delay

variation. However, its limited stopband attenuation (<50 dB) makes it unsuitable for environments with strong

Composite Radar Test Signal Before and After High-Pass Filtering

The Bessel filter exhibited the lowest clutter rejection but maintained superior phase linearity and signal fidelity,

making it suitable for phase-sensitive detection applications.

Therefore, for maximum ground-clutter rejection and high spectral selectivity, the Elliptic filter implemented in

Python provides the most effective performance for radar target detection systems.

The findings of this study demonstrate that the performance of IIR digital filters for ground clutter suppression

The results highlight the fundamental trade-off between selectivity and signal fidelity, a trade-off widely

documented in the digital filter design literature (Wanhammar, 2020).

17. Virtanen, P., Gommers, R., Oliphant, T. E., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific