What is the best way to create and implement a Kaiser bandpass filter subject to certain specified conditions?

I'm trying to create and implement a 100-300 Hz bandpass filter as described in a signal processing research article. Specifically, the paper states, "A finite impulse-response Kaiser filter was used with 30-dB minimal-frequency attenuation in the stop-band, 30 dB maximal frequency loss in the pass-band, and 10 Hz lower/upper transition width."

After a great deal of searching and experimenting, I tried the following:

d = designfilt('bandpassfir', 'StopbandFrequency1', 90, 'PassbandFrequency1', 100, 'PassbandFrequency2', 300, 'StopbandFrequency2', 310, 'StopbandAttenuation1', 30, 'PassbandRipple', 30, 'StopbandAttenuation2', 60, 'SampleRate', 1000, 'DesignMethod', 'kaiserwin');

This "seems" to work insofar as I applied the filter to a random signal, and plotted the magnitude of the Fourier Transform of the result, which was essentially zero for frequencies outside the 100-300 Hz range.

Question 1: Does this appear to be a correct implementation of the approach described in my article?

Question 2: If the answer to (1) is yes, does the designfilt command automatically determine the optimal filter order, n, and parameter, beta, associated with the filter, or should I first estimate these quantities, using the kaiserord function?

Answer 1

[Charantej Pochimireddy]

Hi Paul,

This implementation is correct as far as my knowledge. Generally, designfilt produce minimum-order designs. That is, they generate the smallest filters that satisfy the specified constraints.

Yes, designfilt automatically determines the optimal filter order. You can check the order of the filter using filtord(d). In this case order is 364.

FYI by using Kaiserord you will get same order(364). After getting parameters from Kaiserord you can use fir1 to design the filter. You can use fdatool also to generate the filter. Above three methods will give you same filter( Filtering Data ).