zp2sos

Convert zero-pole-gain filter parameters to second-order sections form

Syntax

[sos,g] = zp2sos(z,p,k)

[sos,g] = zp2sos(z,p,k,order)

[sos,g] = zp2sos(z,p,k,order,scale)

[sos,g] = zp2sos(z,p,k,order,scale,zeroflag)

sos = zp2sos(___)

Description

[sos,g] = zp2sos(z,p,k) finds a second-order section matrix sos with gain g that is equivalent to the transfer function H(z) whose n zeros, m poles, and scalar gain are specified in z, p, and k:

$$H(z)=k\frac{(z-z_1)(z-z_2)\cdots (z-z_n)}{(z-p_1)(z-p_2)\cdots (z-p_m)}.$$

[sos,g] = zp2sos(z,p,k,order) specifies the order of the rows in sos.

[sos,g] = zp2sos(z,p,k,order,scale) specifies the scaling of the gain and numerator coefficients of all second-order sections.

[sos,g] = zp2sos(z,p,k,order,scale,zeroflag) specifies the handling of real zeros that are negatives of each other.

sos = zp2sos(___) embeds the overall system gain in the first section.

Examples

Second-Order Sections from Zero-Pole-Gain Parameters

Design a 5th-order Butterworth lowpass filter using the function butter with output expressed in zero-pole-gain form. Specify the cutoff frequency to be one-fifth of the Nyquist frequency. Convert the result to second-order sections. Visualize the magnitude response.

[z,p,k] = butter(5,0.2);
sos = zp2sos(z,p,k)

sos = 3×6

    0.0013    0.0013         0    1.0000   -0.5095         0
    1.0000    2.0000    1.0000    1.0000   -1.0966    0.3554
    1.0000    2.0000    1.0000    1.0000   -1.3693    0.6926

fvtool(sos)

Input Arguments

z — Zeros

vector

Zeros of the system, specified as a vector. The zeros must be real or come in complex conjugate pairs.

Example: [1 (1+1j)/2 (1-1j)/2]'

Data Types: double
Complex Number Support: Yes

p — Poles

vector

Poles of the system, specified as a vector. The poles must be real or come in complex conjugate pairs.

Example: [1 (1+1j)/2 (1-1j)/2]'

Data Types: double
Complex Number Support: Yes

k — Scalar gain

scalar

Scalar gain of the system, specified as a scalar.

Data Types: double

order — Row order

'up' (default) | 'down'

Row order, specified as one of the following:

• 'up' — Order the sections so the first row of sos contains the poles farthest from the unit circle.

• 'down' — Order the sections so the first row of sos contains the poles closest to the unit circle.

Data Types: char

scale — Scaling of gain and numerator coefficients

'none' (default) | 'inf' | 'two'

Scaling of gain and numerator coefficients, specified as one of the following:

• 'none' — Apply no scaling.

• 'inf' — Apply infinity-norm scaling.

• 'two' — Apply 2-norm scaling.

Using infinity-norm scaling with 'up'-ordering minimizes the probability of overflow in the realization. Using 2-norm scaling with 'down'-ordering minimizes the peak round-off noise.

Note

Infinity-norm and 2-norm scaling are appropriate only for direct-form II implementations.

Data Types: char

zeroflag — Ordering of real zeros

false (default) | true

Ordering of real zeros that are negatives of each other, specified as a logical scalar.

• If you specify zeroflag as false, the function orders those zeros according to proximity to poles.

• If you specify zeroflag as true, the function keeps those zeros together. This option results in a numerator with a middle coefficient equal to zero.

Data Types: logical

Output Arguments

sos — Second-order section representation

matrix

Second-order section representation, returned as a matrix. sos is an L-by-6 matrix

$$\text{sos}=\left[\begin{array}{cccccc}{b}_{01}& b_{11}& b_{21}& 1& a_{11}& a_{21}\\ b_{02}& b_{12}& b_{22}& 1& a_{12}& a_{22}\\ ⋮& ⋮& ⋮& ⋮& ⋮& ⋮\\ b_{0L}& b_{1L}& b_{2L}& 1& a_{1L}& a_{2L}\end{array}\right]$$

whose rows contain the numerator and denominator coefficients b_ik and a_ik of the second-order sections of H(z):

$$H(z)=g{\displaystyle \prod _{k=1}^L{H}_k}(z)=g{\displaystyle \prod _{k=1}^L\frac{b_{0k}+b_{1k}{z}^{-1}+b_{2k}{z}^{-2}}{1+a_{1k}{z}^{-1}+a_{2k}{z}^{-2}}}.$$

If the transfer function has n zeros and m poles, then L is the closest integer greater than or equal to max(n/2,m/2).

g — Overall system gain

real scalar

Overall system gain, returned as a real scalar.

If you call zp2sos with one output argument, the function embeds the overall system gain in the first section, H₁(z), so that

$$H(z)={\displaystyle \prod _{k=1}^L{H}_k(z)}.$$

Note

Embedding the gain in the first section when scaling a direct-form II structure is not recommended and can result in erratic scaling. To avoid embedding the gain, use zp2sos with two outputs.

Algorithms

zp2sos uses a four-step algorithm to determine the second-order section representation for an input zero-pole-gain system:

1. It groups the zeros and poles into complex conjugate pairs using the cplxpair function.

2. It forms the second-order section by matching the pole and zero pairs according to the following rules:

   1. Match the poles closest to the unit circle with the zeros closest to those poles.

   2. Match the poles next closest to the unit circle with the zeros closest to those poles.

   3. Continue until all of the poles and zeros are matched.

   zp2sos groups real poles into sections with the real poles closest to them in absolute value. The same rule holds for real zeros.

3. It orders the sections according to the proximity of the pole pairs to the unit circle. zp2sos normally orders the sections with poles closest to the unit circle last in the cascade. You can tell zp2sos to order the sections in the reverse order using the order argument.

4. zp2sos scales the sections by the norm specified in scale. For arbitrary H(ω), the scaling is defined by

   $${\Vert H\Vert }_p={\left[\frac{1}{2\pi }{\displaystyle \underset{0}{\overset{2\pi }{\int }}{\left|H(\omega )\right|}^{p}d\omega }\right]}^{1/p}$$

   where p can be either infinity or 2. This scaling is an attempt to minimize overflow or peak round-off noise in fixed-point filter implementations.

References

[1] Jackson, L. B. Digital Filters and Signal Processing. 3rd ed. Boston: Kluwer Academic Publishers, 1996.

[2] Mitra, Sanjit Kumar. Digital Signal Processing: A Computer-Based Approach. 3rd ed. New York: McGraw-Hill Higher Education, 2006.

[3] Vaidyanathan, P. P. "Robust Digital Filter Structures." Handbook for Digital Signal Processing (S. K. Mitra and J. F. Kaiser, eds.). New York: John Wiley & Sons, 1993.