FIR filters matlab code

AIM

To verify FIR filters.

EQUIPMENTS:

Operating System         – Windows XP

Constructor                  - Simulator

Software                      - CCStudio 3 & MATLAB 7.5

THEORY:

A Finite Impulse Response (FIR) filter is a discrete linear time-invariant system whose output is based on the weighted summation of a finite number of past inputs. An FIR transversal filter structure can be obtained directly from the equation for discrete-time convolution.

                                           

In this equation, x(k) and y(n) represent the input to and output from the filter at time n.  h(n-k) is the transversal filter coefficients at time n. These coefficients are generated by using FDS (Filter Design Software or Digital filter design package).

FIR – filter is a finite impulse response filter. Order of the filter  should be specified. Infinite response is truncated to get finite impulse response. placing  a window of finite length does this. Types of windows available are Rectangular, Barlett, Hamming, Hanning, Blackmann window etc. This FIR filter is an all zero filter.

PROGRAM:

%fir filt design  window techniques

clc;

clear all;

close all;

rp=input('enter passband ripple');

rs=input('enter the stopband ripple');

fp=input('enter passband freq');

fs=input('enter stopband freq');

f=input('enter sampling freq ');

wp=2*fp/f;

ws=2*fs/f;

num=-20*log10(sqrt(rp*rs))-13;

dem=14.6*(fs-fp)/f;

n=ceil(num/dem);

n1=n+1;

if(rem(n,2)~=0)

    n1=n;

    n=n-1;

end

c=input('enter your choice of window function 1. rectangular 2. triangular 3.kaiser: \n ');

if(c==1)

    y=rectwin(n1);

    disp('Rectangular window filter response');

end

if (c==2)

    y=triang(n1);

   disp('Triangular window filter response');

end

 if(c==3)

     y=kaiser(n1);

   disp('kaiser window filter response');

 end

%LPF

b=fir1(n,wp,y);

[h,o]=freqz(b,1,256);

m=20*log10(abs(h));

subplot(2,2,1);plot(o/pi,m);

title('LPF');

ylabel('Gain in dB-->');

xlabel('(a)         Normalized frequency-->');

%HPF

b=fir1(n,wp,'high',y);

[h,o]=freqz(b,1,256);

m=20*log10(abs(h));

subplot(2,2,2);plot(o/pi,m);

title('HPF');

ylabel('Gain in dB-->');

xlabel('(b)         Normalized frequency-->');

%BPF

wn=[wp ws];

b=fir1(n,wn,y);

[h,o]=freqz(b,1,256);

m=20*log10(abs(h));

subplot(2,2,3);plot(o/pi,m);

title('BPF');

ylabel('Gain in dB-->');

xlabel('(c)         Normalized frequency-->');

%BSF

b=fir1(n,wn,'stop',y);

[h,o]=freqz(b,1,256);

m=20*log10(abs(h));

subplot(2,2,4);plot(o/pi,m);

title('BSF');

ylabel('Gain in dB-->');

xlabel('(d)         Normalized frequency-->');

RESULT: