Hi Axel,
Designing a strict low-pass filter (LPF) with specifications like a 0.5 Hz passband, 1 Hz stopband, 0.01 passband ripple, and 110 dB stopband attenuation is challenging due to the narrow transition band and high attenuation requirements.
The `firpm()` function, using the Parks-McClellan algorithm, is specifically suited for such precise equiripple FIR filter designs. It directly optimizes filter coefficients to meet stringent frequency response criteria, which is why it successfully generates a ~10k tap FIR filter for your specifications. For more information, please refer to the following documentation: https://www.mathworks.com/help/signal/ref/firpm.html
On the other hand, `fdesign.lowpass()` with `design('equiripple')` provides a general framework for filter design but may not handle extreme specifications as robustly as `firpm()`. The narrow transition band and high stopband attenuation can lead to convergence issues, as the algorithm might struggle to find a feasible solution within these constraints. For more information, please refer to the following documentation: https://www.mathworks.com/help/dsp/ref/fdesign.lowpass.html
To address these challenges, consider using `firpm()` directly for your design, as it has proven effective. If you wish to use `fdesign.lowpass()`, you might need to relax the specifications slightly, such as widening the transition band or reducing the stopband attenuation, to aid convergence. Additionally, ensure your system has adequate computational resources to handle the design process, given the high number of filter taps required.
Hope this helps!
