### Bilinear Transformation

The bilinear transform is defined by the substitution

where is some positive constant [83,326]. That is, given a continuous-time transfer function , we apply the bilinear transform by defining

It can be seen that analog dc () maps to digital dc () and
the highest analog frequency () maps to the highest digital
frequency (). It is easy to show that the entire axis
in the plane (where
) is mapped exactly
*once* around the unit circle in the plane (rather than
summing around it infinitely many times, or ``aliasing'' as it does in
ordinary sampling). With real and positive, the left-half
plane maps to the interior of the unit circle, and the right-half
plane maps outside the unit circle. This means *stability is
preserved* when mapping a continuous-time transfer function to
discrete time.

Another valuable property of the bilinear transform is that
*order is preserved*. That is, an th-order -plane transfer
function carries over to an th-order -plane transfer function.
(*Order* in both cases equals the maximum of the degrees of the
numerator and denominator polynomials [449]).^{8.6}

The constant provides one remaining degree of freedom which can be used
to map any particular finite frequency from the axis in the
plane to a particular desired location on the unit circle
in the plane. All other frequencies will be *warped.* In
particular, approaching half the sampling rate, the frequency axis
compresses more and more. Note that at most one resonant frequency can be
preserved under the bilinear transformation of a mass-spring-dashpot
system. On the other hand, filters having a single transition frequency,
such as lowpass or highpass filters, map beautifully under the bilinear
transform; one simply uses to map the cut-off frequency where it
belongs, and the response looks great. In particular, ``equal ripple'' is
preserved for optimal filters of the elliptic and Chebyshev types because
the values taken on by the frequency response are identical in both cases;
only the frequency axis is warped.

The bilinear transform is often used to design digital filters from analog prototype filters [343]. An on-line introduction is given in [449].

#### Finite Difference Approximation vs. Bilinear Transform

Recall that the Finite Difference Approximation (FDA) defines the
elementary differentiator by
(ignoring the
scale factor for now), and this approximates the ideal transfer
function by
. The bilinear transform
calls instead for the transfer function
(again
dropping the scale factor) which introduces a pole at and gives
us the recursion
.
Note that this new pole is right on the unit circle and is therefore
undamped. Any signal energy at half the sampling rate will circulate
forever in the recursion, and due to round-off error, it will tend to
grow. This is therefore a potentially problematic revision of the
differentiator. To get something more practical, we need to specify
that the filter frequency response approximate
over a
*finite range* of frequencies
, where
, above which we allow the response to ``roll off''
to zero. This is how we pose the differentiator problem in terms of
general purpose filter design (see §8.6) [362].

To understand the properties of the finite difference approximation in the frequency domain, we may look at the properties of its -plane to -plane mapping

Setting to 1 for simplicity and solving the FDA mapping for z gives

*inside*the unit circle rather than onto the unit circle in the plane. Solving for the image in the z plane of the axis in the s plane gives

Under the FDA, analog and digital frequency axes coincide well enough at
very low frequencies (high sampling rates), but at high frequencies
relative to the sampling rate, *artificial damping* is introduced as
the image of the axis diverges away from the unit circle.

While the bilinear transform ``warps'' the frequency axis, we can say the
FDA ``doubly warps'' the frequency axis: It has a progressive, compressive
warping in the direction of increasing frequency, like the bilinear
transform, but unlike the bilinear transform, it also warps *normal*
to the frequency axis.

Consider a point traversing the upper half of the unit circle in the z plane, starting at and ending at . At dc, the FDA is perfect, but as we proceed out along the unit circle, we diverge from the axis image and carve an arc somewhere out in the image of the right-half plane. This has the effect of introducing an artificial damping.

Consider, for example, an undamped mass-spring system. There will be a complex conjugate pair of poles on the axis in the plane. After the FDA, those poles will be inside the unit circle, and therefore damped in the digital counterpart. The higher the resonance frequency, the larger the damping. It is even possible for unstable -plane poles to be mapped to stable -plane poles.

In summary, both the bilinear transform and the FDA preserve order, stability, and positive realness. They are both free of aliasing, high frequencies are compressively warped, and both become ideal at dc, or as approaches . However, at frequencies significantly above zero relative to the sampling rate, only the FDA introduces artificial damping. The bilinear transform maps the continuous-time frequency axis in the (the axis) plane precisely to the discrete-time frequency axis in the plane (the unit circle).

**Next Section:**

Application of the Bilinear Transform

**Previous Section:**

Finite Difference Approximation