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# Linear-Phase Fir Filter Design By Least Squares

Module by: Ivan Selesnick. E-mail the author

Summary: This module describes the design of linear-phase FIR filters based on the square error criterion. It includes derivation and an example.

## Linear-Phase FIR Filter Design by Least Squares

This module describes the design of linear-phase FIR filters based on the square error criterion. We will see that FIR filters that minimize the square error can be found by solving a linear system of equations. This technique is straight-forward and is applicable to arbitrary desired frequency responses. In addition, linear constraints on the coefficients are easily included.

Linear-phase filter design by least squares has several advantages.

• Optimal with respect to square error criterion.
• Simple, non-iterative method.
• Analytic solutions sometimes possible, otherwise solution is obtained via solution to linear system of equations.
• Allows the use of a frequency dependent weighting function.
• Suitable for arbitrary Dω D ω and Wω W ω .
• Easy to include arbitrary linear constraints.

### Derivation

The weighted integral square error (or " L 2 L 2 error") is defined by

ε 2 =0πWωAωDω2d ω ε 2 ω 0 W ω A ω D ω 2
(1)
where
• Aω A ω : the actual amplitude response
• Dω D ω : the ideal amplitude response
• Wω W ω : nonnegative weighting function

The weighting function can be used to assign more importance to specific parts of the frequency response. For example, it is common to weight the stop-band more heavily than the pass-band. Once the length NN and the Type of the filter (I, II, III, IV) is chosen, the goal is to find the filter coefficients hn h n that minimizes ε 2 ε 2 . To develop this approach, we will consider the design of Type I FIR filters. The design of the other 3 linear-phase FIR filter types can be developed similarly.

Recall that for a Type I FIR filter, the amplitude response if given by Aω= n =0Mancosnω A ω n 0 M a n n ω where a0=hM a 0 h M an=2hMn a n 2 h M n 1nM 1 n M M=N12 M N 1 2

To obtain the coefficients an a n to minimize ε 2 ε 2 , we can set the derivatives equal to zero, dd ak ε 2 =0 a k ε 2 0 0kM 0 k M The derivatives of ε 2 ε 2 with respect to ak a k can be found as:

dd ak ε 2 =0πdWωAωDω2d ak d ω =20πWω(AωDω)dAωd ak d ω =20πWω(AωDω)coskωd ω a k ε 2 ω 0 a k W ω A ω D ω 2 2 ω 0 W ω A ω D ω a k A ω 2 ω 0 W ω A ω D ω k ω
(2)
Therefore, dd ak ε 2 =0 a k ε 2 0 becomes 0πWωAωcoskωd ω =0πWωDωcoskωd ω ω 0 W ω A ω k ω ω 0 W ω D ω k ω or 0πWω n =0Mancosnωcoskωd ω =0πWωDωcoskωd ω ω 0 W ω n 0 M a n n ω k ω ω 0 W ω D ω k ω or n =0Man0πWωcosnωcoskωd ω =0πWωDωcoskωd ω n 0 M a n ω 0 W ω n ω k ω ω 0 W ω D ω k ω If we define
Qkn=1π0πWωcosnωcoskωd ω Q k n 1 ω 0 W ω n ω k ω
(3)
and
bk=1π0πWωDωcoskωd ω b k 1 ω 0 W ω D ω k ω
(4)
then the derivative conditions can be written as
n =0MQknan=bk n 0 M Q k n a n b k
(5)
where 0kM 0 k M This is a linear system of equations, and can be written in matrix form as ( Q00Q01Q0M Q10Q11Q1M QM0QM1QMM )( a0 a1 aM )=( b0 b1 bM ) Q 0 0 Q 0 1 Q 0 M Q 1 0 Q 1 1 Q 1 M Q M 0 Q M 1 Q M M a 0 a 1 a M b 0 b 1 b M or Qa=b Q a b Therefore, the Type 1 FIR filter that minimizes the square error can be obtained by solving this linear system of equations. a=Q-1b a Q b

### Structure of the Matrix Q

It turns out that the matrix QQ has a special structure. Note that

cosnωcoskω=12cos(kn)ω+12cos(k+n)ω n ω k ω 1 2 k n ω 1 2 k n ω
(6)
so that
Qkn=1π0πWωcosnωcoskωd ω =12π0πWωcos(kn)ωd ω +12π0πWωcos(k+n)ωd ω Q k n 1 ω 0 W ω n ω k ω 1 2 ω 0 W ω k n ω 1 2 ω 0 W ω k n ω
(7)
or
Qkn=12 Q 1 kn+12 Q 2 kn Q k n 1 2 Q 1 k n 1 2 Q 2 k n
(8)
where Q 1 Q 1 and Q 2 Q 2 are defined as
Q 1 kn=1π0πWωcos(kn)ωd ω Q 1 k n 1 ω 0 W ω k n ω
(9)
and
Q 2 kn=1π0πWωcos(k+n)ωd ω Q 2 k n 1 ω 0 W ω k n ω
(10)
Accordingly, we can write
Q 1 kn=qkn Q 1 k n q k n
(11)
Q 2 kn=qk+n Q 2 k n q k n
(12)
where
qn=1π0πWωcosnωd ω q n 1 ω 0 W ω n ω
(13)
With this notation, the matrices Q 1 Q 1 and Q 2 Q 2 are written as
Q 1 =( q0q1qM q1q0qM1 qMqM1q0 ) Q 1 q 0 q 1 q M q 1 q 0 q M 1 q M q M 1 q 0
(14)
and
Q 2 =( q0q1qM q1q2qM+1 qMqM+1q2M ) Q 2 q 0 q 1 q M q 1 q 2 q M 1 q M q M 1 q 2 M
(15)
Note that we have used qn=qn q n q n here. The matrix Q 1 Q 1 is a symmetric Toeplitz matrix (constant along its diagonals), and the matrix Q 2 Q 2 is a Hankel matrix (constant along its anti-diagonals). Consequently,
1. the matrices can be stored with less memory than arbitrary matrices ( 2M+1 2 M 1 numbers instead of M+12 M 1 2 numbers),
2. there are fast algorithms to compute the solution to 'Toeplitz plus Hankel' systems with computational complexity OM2 O M 2 instead of OM3 O M 3 . (In fact, the complexity can be reduced further, but with higher overhead.)

### Relation to the DTFT

To express qk q k and bk b k using the inverse Fourier transform, extend Dω D ω and Wω W ω symmetrically, so that Dω=Dω D ω D ω and Wω=Wω W ω W ω . Then we can write

qn=12πππWωcosnωd ω q n 1 2 ω W ω n ω
(16)
As sine is an anti-symmetric function
ππWωsinnωd ω =0 ω W ω n ω 0
(17)
so we can write
qn=12πππWω(cosnω+isinnω)d ω q n 1 2 ω W ω n ω n ω
(18)
or
qn=12πππWωeinωd ω q n 1 2 ω W ω n ω
(19)
which we recognize as the inverse discrete-time Fourier transform
qn=DTFT-1Wω q n DTFT W ω
(20)
Similarly,
qn=DTFT-1WωDω q n DTFT W ω D ω
(21)

### Low-Pass: Weighted Square Error

The weighting function Wω W ω can be used to improve the FIR low-pass filter because

1. it allows you to eliminate Gibbs phenomenon by deleting a neighborhood around the band edge, and
2. it allows you to assign different weights to the pass-band and the stop-band.
For example, if the ideal low-pass amplitude response is as shown in Figure 1.

and if the weighting function is as shown, Figure 2.

where ω p < ω o < ω s ω p ω o ω s , then the square error criterion becomes

ε 2 =0 ω p Aω12d ω +K ω s πA2ωd ω ε 2 ω 0 ω p A ω 1 2 K ω ω s A ω 2
(22)
To find the matrix QQ and the vector bb to this weighting function it is useful to recall
1π ω 1 ω 2 cosnωd ω = ω 2 πsinc ω 2 πn ω 1 πsinc ω 1 πn 1 ω ω 1 ω 2 n ω ω 2 sinc ω 2 n ω 1 sinc ω 1 n
(23)
Then
qk=1π0πWωcoskωd ω =1π0 ω p coskωd ω +Kπ ω s πcoskωd ω ={ ω p π+K(1 ω s π)  if  k=0 ω p πsinc ω p πkK ω s πsinc ω s πk  if  k0 q k 1 ω 0 W ω k ω 1 ω 0 ω p k ω K ω ω s k ω ω p K 1 ω s k 0 ω p sinc ω p k K ω s sinc ω s k k 0
(24)
Similarly,
bk=1π0πWωDωcoskωd ω =1π0 ω p coskωd ω = ω p πsinc ω p πk b k 1 ω 0 W ω D ω k ω 1 ω 0 ω p k ω ω p sinc ω p k
(25)

### Weighted Low-Pass Example

In the following example, we design a Type 1 FIR low-pass filter of length 31, with band-edges ω p =0.26π ω p 0.26 , ω s =0.34π ω s 0.34 and a stop-band weight of K=10 K 10 . (See Figure 3)

Compared to the Impulse Response Truncation (IRT) method the peak error is reduced. The filter was designed using the following Matlab code.



% WEIGHTED LEAST SQUARE LOWPASS FILTER

% filter length
N = 31;
M = (N-1)/2;

% set band-edges and stop-band weighting
wp = 0.26*pi;
ws = 0.34*pi;
K = 10;

% normalize band-edges for convenience
fp = wp/pi;
fs = ws/pi;

% construct q(k)
q  = [fp+K*(1-fs), fp*sinc(fp*[1:2*M])-K*fs*sinc(fs*[1:2*M])];

% construct Q1, Q2, Q
Q1 = toeplitz(q([0:M]+1));
Q2 = hankel(q([0:M]+1),q([M:2*M]+1));
Q  = (Q1 + Q2)/2;

% construct b
b  = fp*sinc(fp*[0:M]');

% solve linear system to get a(n)
a  = Q\b;

% form impulse response h(n)
h  = [a(M+1:-1:2)/2; a(1); a(2:M+1)/2];



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