Previous two blogs demonstrated the design of Digital IIR Filters. However, IIR filters are not stable due their recursive nature. Also, they have non-linear phase response hence, the output of the filter appears to be distorted version of the input.
To eliminate these issues FIR filters are used. They have Linear Phase response and hence, output of the filter is the phase shifted input signal. Also, since they don't have any feedback, the filters are very stable. However, this requires large memory as compared to IIR filters.
One of the method to design FIR filters is using Windowing Method. Other method is using FSM which will be shown in the next post. In this method, a particular window function is chosen on the basis of the attenuation required in the stop band. Using this window function, the coefficients of the filter is found and thus filter is designed.
To eliminate these issues FIR filters are used. They have Linear Phase response and hence, output of the filter is the phase shifted input signal. Also, since they don't have any feedback, the filters are very stable. However, this requires large memory as compared to IIR filters.
One of the method to design FIR filters is using Windowing Method. Other method is using FSM which will be shown in the next post. In this method, a particular window function is chosen on the basis of the attenuation required in the stop band. Using this window function, the coefficients of the filter is found and thus filter is designed.
well written!
ReplyDeleteThank you
DeleteWindowing function is helpful for optimum design since the appropriate window function is chosen using value of As from the input data.
ReplyDeleteYes, the range of As for various Window function are already derived.
DeleteThe system chooses the window function on its own! This helps in efficient filter design.
ReplyDeleteYes, and hence, it is user-friendly as well!
DeleteFIR implementation is basically trying to perform convolution of the input samples with the stored samples of the sinc (filter taps) function.
ReplyDeleteHence, it requires large amount of memory as compared to IIR Filters
DeleteAlso, since the signal has to be truncated side lobes are present in FIR filters
Deleteeasy to design and very helpful
ReplyDeleteHowever, FSM is easier to design
DeleteWindow function should be selected on the basis of transition band width.
ReplyDeleteYes, also Attenuation in stop band is also one of the major factors in the selection
DeleteCompared to IIR filters, FIR filters sometimes have the disadvantage that they require more
ReplyDeletememory and/or calculation to achieve a given filter response characteristic.
Yes, memory is a major issue in FIR Filters
DeleteHowever, since they are always stable, designing issues are reduced
DeleteWindowing is one of the better methods to implement digital filters.
ReplyDeleteYes, however, for low-end applications FSm can be preferred.
DeleteWindow functions used here were blackman, bartlet, rectangular, hamming and hanning
ReplyDeleteHOwever, Blackman is mostly used for high As
Delete