Linear phase FIR filter design using windowing method

In this experiment we designed Linear phase FIR filter using Scilab. These are filters that have a Finite Impulse Response. Broadly, the method for designing the filter is the same as IIR filters. Filter parameters like Attenuation in Stop band (As) and Pass band (Ap) as well as Pass band frequency, Stop band frequency and sampling frequency were given as input. You input the formulae in a Scilab code and run the program. Window function include Hamming window, Bartlet Window, Hanning window, Blackman window etc. We used a Hanning Window as the window function and wrote the code accordingly. The difference between IIR and FIR designing is that much of the calculation is done in time domain rather than the transform domain..

Comments

  1. Unknown24 April 2017 at 10:38

    We can even directly call the required window function by using the window() scilab function

    ReplyDelete
  2. Unknown25 April 2017 at 22:43

    Well explained.

    ReplyDelete
  3. $oM€$h26 April 2017 at 18:25

    It is one the best methods to implement FIR filters

    ReplyDelete
  4. Unknown27 April 2017 at 08:54

    According to value of As, window function can be selected

    ReplyDelete
    Replies
    1. Unknown27 April 2017 at 10:41

      As of the window function should be greater than the given As

      Delete
  5. Parth1 May 2017 at 06:59

    Window functions are preferable because they allow different window functions
    which can be used as per the design specifications of filter

    ReplyDelete

Post a Comment

Popular posts from this blog

FIR Filter Design using FSM

A filter is essentially a system or a network that selectively changes the wave shape, amplitude–frequency and/or phase-frequency characteristics of a signal in desired manner. Common filtering objectives are to improve the quality of a signal, to extract information from signal or to separate two or more signals previously combined.  FSM for FIR filter  design is perhaps the simplest and most direct technique when a desired frequency response  has been specified. T he given frequency response is sampled at a set of equally spaced frequencies to obtain N samples. Thus , sampling the continuous frequency response Hd(w) at N points essentially gives us the N-point DFT of Hd(2pnk/N). Thus by using the IDFT formula, the filter co-efficients can be calculated. One way to reduce the error is to increase the number of frequency samples. Thus we FIR filter design using Frequency Sampling Method and noted the output.
Read more

Review on Patent

Signal processing system and method for enhanced cascaded integrator-comb interpolation filter stabilization   Patent No.  US 5880973 A A signal processing system and method for stabilizing cascaded integrator-comb (CIC) interpolation filters. The signal processing system includes an overflow detection circuit and reset circuitry. The overflow detection circuit monitors the output of the CIC interpolation filter for an overflow condition which is indicative of filter instability. When an overflow condition is detected, the reset circuitry automatically resets the filter, thereby eliminating the instability.  ransmission filter 102 first interpolates the input samples (31.5 MHz samples on bus 112) by a factor of 4, as well as shapes the spectral response. In general, implementation of transmission filter 102 is well known in the art. For example, a conventional finite impulse response (FIR) filter, with one or two stages, can be used. With respect to a QPSK-based cellu...
Read more