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Convert digital filter transfer function data to second-order sections form

`[sos,g] = tf2sos(b,a)`

[sos,g] = tf2sos(b,a,* 'order'*)

[sos,g] = tf2sos(b,a,

`'order'`

`'scale'`

sos = tf2sos(...)

`tf2sos`

converts a transfer function representation
of a given digital filter to an equivalent second-order section representation.

`[sos,g] = tf2sos(b,a)`

finds
a matrix `sos`

in second-order section form with
gain `g`

that is equivalent to the digital filter
represented by transfer function coefficient vectors `a`

and `b`

.

$$H(z)=\frac{B(z)}{A(z)}=\frac{{b}_{1}+{b}_{2}{z}^{-1}+\cdots +{b}_{n+1}{z}^{-n}}{{a}_{1}+{a}_{2}{z}^{-1}+\cdots +{a}_{m+1}{z}^{-m}}$$

`sos`

is an *L*-by-6 matrix

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

whose rows contain the numerator and denominator coefficients
*b _{ik}* and

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

`[sos,g] = tf2sos(b,a,`

specifies
the order of the rows in * 'order'*)

`sos`

, where `'order'`

`'down'`

, to order the sections so the first row of`sos`

contains the poles closest to the unit circle`'up'`

, to order the sections so the first row of`sos`

contains the poles farthest from the unit circle (default)

`[sos,g] = tf2sos(b,a,`

specifies
the desired scaling of the gain and numerator coefficients of all
second-order sections, where * 'order'*,

`'scale'`

`'scale'`

`'none'`

, to apply no scaling (default)`'inf'`

, to apply infinity-norm scaling`'two'`

, to apply 2-norm scaling

Using infinity-norm scaling in conjunction with `up`

-ordering
minimizes the probability of overflow in the realization. Using 2-norm
scaling in conjunction with `down`

-ordering minimizes
the peak round-off noise.

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

`sos = tf2sos(...)`

embeds
the overall system gain, `g`

, in the first section, *H _{1}*(

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

Embedding the gain in the first section when scaling a direct-form
II structure is not recommended and may result in erratic scaling.
To avoid embedding the gain, use `ss2sos`

with
two outputs.

`tf2sos`

uses a four-step algorithm to determine
the second-order section representation for an input transfer function
system:

It finds the poles and zeros of the system given by

`b`

and`a`

.It uses the function

`zp2sos`

, which first groups the zeros and poles into complex conjugate pairs using the`cplxpair`

function.`zp2sos`

then forms the second-order sections by matching the pole and zero pairs according to the following rules:Match the poles closest to the unit circle with the zeros closest to those poles.

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

Continue until all of the poles and zeros are matched.

`tf2sos`

groups real poles into sections with the real poles closest to them in absolute value. The same rule holds for real zeros.It orders the sections according to the proximity of the pole pairs to the unit circle.

`tf2sos`

normally orders the sections with poles closest to the unit circle last in the cascade. You can tell`tf2sos`

to order the sections in the reverse order by specifying the`'down'`

flag.`tf2sos`

scales the sections by the norm specified in theargument. For arbitrary`'scale'`

*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 ∞ or 2. See the references for details on the scaling. This scaling is an attempt to minimize overflow or peak round-off noise in fixed point filter implementations.

[1] Jackson, L. B. *Digital Filters
and Signal Processing*. 3rd ed. Boston: Kluwer Academic
Publishers, 1996, chap. 11.

[2] Mitra, S. K. *Digital Signal
Processing: A Computer-Based Approach*. New York: McGraw-Hill,
1998, chap. 9.

[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, chap. 7.