# cheby1

Chebyshev Type I filter design

## Syntax

• [b,a] = cheby1(n,Rp,Wp) example
• [b,a] = cheby1(n,Rp,Wp,ftype) example
• [z,p,k] = cheby1(___) example
• [A,B,C,D] = cheby1(___) example
• [___] = cheby1(___,'s') example

## Description

example

[b,a] = cheby1(n,Rp,Wp) returns the transfer function coefficients of an nth-order lowpass digital Chebyshev Type I filter with normalized passband edge frequency Wp and Rp decibels of peak-to-peak passband ripple.

example

[b,a] = cheby1(n,Rp,Wp,ftype) designs a lowpass, highpass, bandpass, or bandstop Chebyshev Type I filter, depending on the value of ftype and the number of elements of Wp. The resulting bandpass and bandstop designs are of order 2n.
 Note:   See Limitations for information about numerical issues that affect forming the transfer function.

example

[z,p,k] = cheby1(___) designs a lowpass, highpass, bandpass, or bandstop digital Chebyshev Type I filter and returns its zeros, poles, and gain. This syntax can include any of the input arguments in previous syntaxes.

example

[A,B,C,D] = cheby1(___) designs a lowpass, highpass, bandpass, or bandstop digital Chebyshev Type I filter and returns the matrices that specify its state-space representation.

example

[___] = cheby1(___,'s') designs a lowpass, highpass, bandpass, or bandstop analog Chebyshev Type I filter with passband edge angular frequency Wp and Rp decibels of passband ripple.

## Examples

collapse all

### Lowpass Chebyshev Type I Transfer Function

Design a 6th-order lowpass Chebyshev Type I filter with 10 dB of passband ripple and a passband edge frequency of 300 Hz, which, for data sampled at 1000 Hz, corresponds to rad/sample. Plot its magnitude and phase responses. Use it to filter a 1000-sample random signal.

[b,a] = cheby1(6,10,0.6); freqz(b,a) dataIn = randn(1000,1); dataOut = filter(b,a,dataIn); 

### Bandstop Chebyshev Type I Filter

Design a 6th-order Chebyshev Type I bandstop filter with normalized edge frequencies of and rad/sample and 5 dB of passband ripple. Plot its magnitude and phase responses. Use it to filter random data.

[b,a] = cheby1(3,5,[0.2 0.6],'stop'); freqz(b,a) dataIn = randn(1000,1); dataOut = filter(b,a,dataIn); 

### Highpass Chebyshev Type I Filter

Design a 9th-order highpass Chebyshev Type I filter with 0.5 dB of passband ripple and a passband edge frequency of 300 Hz, which, for data sampled at 1000 Hz, corresponds to rad/sample. Plot the magnitude and phase responses. Convert the zeros, poles, and gain to second-order sections for use by fvtool.

[z,p,k] = cheby1(9,0.5,300/500,'high'); sos = zp2sos(z,p,k); fvtool(sos,'Analysis','freq') 

### Bandpass Chebyshev Type I Filter

Design a 20th-order Chebyshev Type I bandpass filter with a lower passband frequency of 500 Hz and a higher passband frequency of 560 Hz. Specify a passband ripple of 3 dB and a sample rate of 1500 Hz. Use the state-space representation. Design an identical filter using designfilt.

[A,B,C,D] = cheby1(10,3,[500 560]/750); d = designfilt('bandpassiir','FilterOrder',20, ... 'PassbandFrequency1',500,'PassbandFrequency2',560, ... 'PassbandRipple',3,'SampleRate',1500); 

Convert the state-space representation to second-order sections. Visualize the frequency responses using fvtool.

sos = ss2sos(A,B,C,D); fvt = fvtool(sos,d,'Fs',1500); legend(fvt,'cheby1','designfilt') 

### Comparison of Analog IIR Lowpass Filters

Design a 5th-order analog Butterworth lowpass filter with a cutoff frequency of 2 GHz. Multiply by to convert the frequency to radians per second. Compute the frequency response of the filter at 4096 points.

n = 5; f = 2e9; [zb,pb,kb] = butter(n,2*pi*f,'s'); [bb,ab] = zp2tf(zb,pb,kb); [hb,wb] = freqs(bb,ab,4096); 

Design a 5th-order Chebyshev Type I filter with the same edge frequency and 3 dB of passband ripple. Compute its frequency response.

[z1,p1,k1] = cheby1(n,3,2*pi*f,'s'); [b1,a1] = zp2tf(z1,p1,k1); [h1,w1] = freqs(b1,a1,4096); 

Design a 5th-order Chebyshev Type II filter with the same edge frequency and 30 dB of stopband attenuation. Compute its frequency response.

[z2,p2,k2] = cheby2(n,30,2*pi*f,'s'); [b2,a2] = zp2tf(z2,p2,k2); [h2,w2] = freqs(b2,a2,4096); 

Design a 5th-order elliptic filter with the same edge frequency, 3 dB of passband ripple, and 30 dB of stopband attenuation. Compute its frequency response.

[ze,pe,ke] = ellip(n,3,30,2*pi*f,'s'); [be,ae] = zp2tf(ze,pe,ke); [he,we] = freqs(be,ae,4096); 

Plot the attenuation in decibels. Express the frequency in gigahertz. Compare the filters.

plot(wb/(2e9*pi),mag2db(abs(hb))) hold on plot(w1/(2e9*pi),mag2db(abs(h1))) plot(w2/(2e9*pi),mag2db(abs(h2))) plot(we/(2e9*pi),mag2db(abs(he))) axis([0 4 -40 5]) grid xlabel('Frequency (GHz)') ylabel('Attenuation (dB)') legend('butter','cheby1','cheby2','ellip') 

The Butterworth and Chebyshev Type II filters have flat passbands and wide transition bands. The Chebyshev Type I and elliptic filters roll off faster but have passband ripple. The frequency input to the Chebyshev Type II design function sets the beginning of the stopband rather than the end of the passband.

## Input Arguments

collapse all

### n — Filter orderinteger scalar

Filter order, specified as an integer scalar.

Data Types: double

### Rp — Peak-to-peak passband ripplepositive scalar

Peak-to-peak passband ripple, specified as a positive scalar expressed in decibels.

If your specification, ℓ, is in linear units, you can convert it to decibels using Rp = 40 log10((1+ℓ)/(1–ℓ)).

Data Types: double

### Wp — Passband edge frequencyscalar | two-element vector

Passband edge frequency, specified as a scalar or a two-element vector. The passband edge frequency is the frequency at which the magnitude response of the filter is –Rp decibels. Smaller values of passband ripple, Rp, result in wider transition bands.

• If Wp is a scalar, then cheby1 designs a lowpass or highpass filter with edge frequency Wp.

If Wp is the two-element vector [w1 w2], where w1 < w2, then cheby1 designs a bandpass or bandstop filter with lower edge frequency w1 and higher edge frequency w2.

• For digital filters, the passband edge frequencies must lie between 0 and 1, where 1 corresponds to the Nyquist rate—half the sample rate or π rad/sample.

For analog filters, the passband edge frequencies must be expressed in radians per second and can take on any positive value.

Data Types: double

### ftype — Filter type'low' | 'bandpass' | 'high' | 'stop'

Filter type, specified as a string.

• 'low' specifies a lowpass filter with passband edge frequency Wp. 'low' is the default for scalar Wp.

• 'high' specifies a highpass filter with passband edge frequency Wp.

• 'bandpass' specifies a bandpass filter of order 2n if Wp is a two-element vector. 'bandpass' is the default when Wp has two elements.

• 'stop' specifies a bandstop filter of order 2n if Wp is a two-element vector.

Data Types: char

## Output Arguments

collapse all

### b,a — Transfer function coefficientsrow vectors

Transfer function coefficients of the filter, returned as row vectors of length n + 1 for lowpass and highpass filters and 2n + 1 for bandpass and bandstop filters.

• For digital filters, the transfer function is expressed in terms of b and a as

$H\left(z\right)=\frac{B\left(z\right)}{A\left(z\right)}=\frac{\text{b(1)}+\text{b(2)}\text{\hspace{0.17em}}{z}^{-1}+\cdots +\text{b(n+1)}\text{\hspace{0.17em}}{z}^{-n}}{\text{a(1)}+\text{a(2)}\text{\hspace{0.17em}}{z}^{-1}+\cdots +\text{a(n+1)}\text{\hspace{0.17em}}{z}^{-n}}.$

• For analog filters, the transfer function is expressed in terms of b and a as

$H\left(s\right)=\frac{B\left(s\right)}{A\left(s\right)}=\frac{\text{b(1)}\text{\hspace{0.17em}}{s}^{n}+\text{b(2)}\text{\hspace{0.17em}}{s}^{n-1}+\cdots +\text{b(n+1)}}{\text{a(1)}\text{\hspace{0.17em}}{s}^{n}+\text{a(2)}\text{\hspace{0.17em}}{s}^{n-1}+\cdots +\text{a(n+1)}}.$

Data Types: double

### z,p,k — Zeros, poles, and gaincolumn vectors, scalar

Zeros, poles, and gain of the filter, returned as two column vectors of length n (2n for bandpass and bandstop designs) and a scalar.

• For digital filters, the transfer function is expressed in terms of z, p, and k as

$H\left(z\right)=\text{k}\frac{\left(1-\text{z(1)}\text{\hspace{0.17em}}{z}^{-1}\right)\text{\hspace{0.17em}}\left(1-\text{z(2)}\text{\hspace{0.17em}}{z}^{-1}\right)\cdots \left(1-\text{z(n)}\text{\hspace{0.17em}}{z}^{-1}\right)}{\left(1-\text{p(1)}\text{\hspace{0.17em}}{z}^{-1}\right)\text{\hspace{0.17em}}\left(1-\text{p(2)}\text{\hspace{0.17em}}{z}^{-1}\right)\cdots \left(1-\text{p(n)}\text{\hspace{0.17em}}{z}^{-1}\right)}.$

• For analog filters, the transfer function is expressed in terms of z, p, and k as

$H\left(s\right)=\text{k}\frac{\left(s-\text{z(1)}\right)\text{\hspace{0.17em}}\left(s-\text{z(2)}\right)\cdots \left(s-\text{z(n)}\right)}{\left(s-\text{p(1)}\right)\text{\hspace{0.17em}}\left(s-\text{p(2)}\right)\cdots \left(s-\text{p(n)}\right)}.$

Data Types: double

### A,B,C,D — State-space matricesmatrices

State-space representation of the filter, returned as matrices. If m = n for lowpass and highpass designs and m = 2n for bandpass and bandstop filters, then A is m × m, B is m × 1, C is 1 × m, and D is 1 × 1.

• For digital filters, the state-space matrices relate the state vector x, the input u, and the output y through

$\begin{array}{c}x\left(k+1\right)=\text{A}\text{\hspace{0.17em}}x\left(k\right)+\text{B}\text{\hspace{0.17em}}u\left(k\right)\\ y\left(k\right)=\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{C}\text{\hspace{0.17em}}x\left(k\right)+\text{D}\text{\hspace{0.17em}}u\left(k\right).\end{array}$

• For analog filters, the state-space matrices relate the state vector x, the input u, and the output y through

$\begin{array}{l}\stackrel{˙}{x}=\text{A}\text{\hspace{0.17em}}x+\text{B}\text{\hspace{0.17em}}u\\ y=\text{C}\text{\hspace{0.17em}}x+\text{D}\text{\hspace{0.17em}}u.\end{array}$

Data Types: double

collapse all

### Limitations

Numerical Instability of Transfer Function Syntax

In general, use the [z,p,k] syntax to design IIR filters. To analyze or implement your filter, you can then use the [z,p,k] output with zp2sos. If you design the filter using the [b,a] syntax, you might encounter numerical problems. These problems are due to round-off errors and can occur for n as low as 4. The following example illustrates this limitation.

n = 6; Rp = 0.1; Wn = [2.5e6 29e6]/500e6; ftype = 'bandpass'; % Transfer function design [b,a] = cheby1(n,Rp,Wn,ftype); % This filter is unstable % Zero-pole-gain design [z,p,k] = cheby1(n,Rp,Wn,ftype); sos = zp2sos(z,p,k); % Plot and compare the results hfvt = fvtool(b,a,sos,'FrequencyScale','log'); legend(hfvt,'TF Design','ZPK Design') 

### Algorithms

Chebyshev Type I filters are equiripple in the passband and monotonic in the stopband. Type I filters roll off faster than Type II filters, but at the expense of greater deviation from unity in the passband.

cheby1 uses a five-step algorithm:

1. It finds the lowpass analog prototype poles, zeros, and gain using the function cheb1ap.

2. It converts the poles, zeros, and gain into state-space form.

3. If required, it uses a state-space transformation to convert the lowpass filter to a highpass, bandpass, or bandstop filter with the desired frequency constraints.

4. For digital filter design, it uses bilinear to convert the analog filter into a digital filter through a bilinear transformation with frequency prewarping. Careful frequency adjustment enables the analog filters and the digital filters to have the same frequency response magnitude at Wp or w1 and w2.

5. It converts the state-space filter back to transfer function or zero-pole-gain form, as required.