Ztransform
In mathematics and signal processing, the Ztransform converts a discretetime signal, which is a sequence of real or complex numbers, into a complex frequency domain representation.
It can be considered as a discretetime equivalent of the Laplace transform. This similarity is explored in the theory of time scale calculus.
Contents
 1 History
 2 Definition
 3 Inverse Ztransform
 4 Region of convergence
 5 Properties
 6 Table of common Ztransform pairs
 7 Relationship to Fourier series and Fourier transform
 8 Relationship to Laplace transform
 9 Linear constantcoefficient difference equation
 10 See also
 11 References
 12 Further reading
 13 External links
History
The basic idea now known as the Ztransform was known to Laplace, and reintroduced in 1947 by W. Hurewicz as a tractable way to solve linear, constantcoefficient difference equations.^{1} It was later dubbed "the ztransform" by Ragazzini and Zadeh in the sampleddata control group at Columbia University in 1952.^{2}^{3}
The modified or advanced Ztransform was later developed and popularized by E. I. Jury.^{4}^{5}
The idea contained within the Ztransform is also known in mathematical literature as the method of generating functions which can be traced back as early as 1730 when it was introduced by de Moivre in conjunction with probability theory.^{6} From a mathematical view the Ztransform can also be viewed as a Laurent series where one views the sequence of numbers under consideration as the (Laurent) expansion of an analytic function.
Definition
The Ztransform, like many integral transforms, can be defined as either a onesided or twosided transform.
Bilateral Ztransform
The bilateral or twosided Ztransform of a discretetime signal x[n] is the formal power series X(z) defined as
where n is an integer and z is, in general, a complex number:
where A is the magnitude of z, j is the imaginary unit, and ɸ is the complex argument (also referred to as angle or phase) in radians.
Unilateral Ztransform
Alternatively, in cases where x[n] is defined only for n ≥ 0, the singlesided or unilateral Ztransform is defined as
In signal processing, this definition can be used to evaluate the Ztransform of the unit impulse response of a discretetime causal system.
An important example of the unilateral Ztransform is the probabilitygenerating function, where the component x[n] is the probability that a discrete random variable takes the value n, and the function X(z) is usually written as X(s), in terms of s = z^{−1}. The properties of Ztransforms (below) have useful interpretations in the context of probability theory.
Geophysical definition
In geophysics, the usual definition for the Ztransform is a power series in z as opposed to z^{−1}. This convention is used, for example, by Robinson and Treitel^{7} and by Kanasewich.^{8} The geophysical definition is:
The two definitions are equivalent; however, the difference results in a number of changes. For example, the location of zeros and poles move from inside the unit circle using one definition, to outside the unit circle using the other definition.^{7}^{8} Thus, care is required to note which definition is being used by a particular author.
Inverse Ztransform
The inverse Ztransform is
where C is a counterclockwise closed path encircling the origin and entirely in the region of convergence (ROC). In the case where the ROC is causal (see Example 2), this means the path C must encircle all of the poles of X(z).
A special case of this contour integral occurs when C is the unit circle (and can be used when the ROC includes the unit circle which is always guaranteed when X(z) is stable, i.e. all the poles are within the unit circle). The inverse Ztransform simplifies to the inverse discretetime Fourier transform:
The Ztransform with a finite range of n and a finite number of uniformly spaced z values can be computed efficiently via Bluestein's FFT algorithm. The discretetime Fourier transform (DTFT)—not to be confused with the discrete Fourier transform (DFT)—is a special case of such a Ztransform obtained by restricting z to lie on the unit circle.
Region of convergence
The region of convergence (ROC) is the set of points in the complex plane for which the Ztransform summation converges.
Example 1 (no ROC)
Let x[n] = (0.5)^{n}. Expanding x[n] on the interval (−∞, ∞) it becomes
Looking at the sum
Therefore, there are no values of z that satisfy this condition.
Example 2 (causal ROC)
Let (where u is the Heaviside step function). Expanding x[n] on the interval (−∞, ∞) it becomes
Looking at the sum
The last equality arises from the infinite geometric series and the equality only holds if 0.5z^{−1} < 1 which can be rewritten in terms of z as z > 0.5. Thus, the ROC is z > 0.5. In this case the ROC is the complex plane with a disc of radius 0.5 at the origin "punched out".
Example 3 (anticausal ROC)
Let (where u is the Heaviside step function). Expanding x[n] on the interval (−∞, ∞) it becomes
Looking at the sum
Using the infinite geometric series, again, the equality only holds if 0.5^{−1}z < 1 which can be rewritten in terms of z as z < 0.5. Thus, the ROC is z < 0.5. In this case the ROC is a disc centered at the origin and of radius 0.5.
What differentiates this example from the previous example is only the ROC. This is intentional to demonstrate that the transform result alone is insufficient.
Examples conclusion
Examples 2 & 3 clearly show that the Ztransform X(z) of x[n] is unique when and only when specifying the ROC. Creating the polezero plot for the causal and anticausal case show that the ROC for either case does not include the pole that is at 0.5. This extends to cases with multiple poles: the ROC will never contain poles.
In example 2, the causal system yields an ROC that includes z = ∞ while the anticausal system in example 3 yields an ROC that includes z = 0.
In systems with multiple poles it is possible to have an ROC that includes neither z = ∞ nor z = 0. The ROC creates a circular band. For example,
has poles at 0.5 and 0.75. The ROC will be 0.5 < z < 0.75, which includes neither the origin nor infinity. Such a system is called a mixedcausality system as it contains a causal term (0.5)^{n}un and an anticausal term −(0.75)^{n}u[−n−1].
The stability of a system can also be determined by knowing the ROC alone. If the ROC contains the unit circle (i.e., z = 1) then the system is stable. In the above systems the causal system (Example 2) is stable because z > 0.5 contains the unit circle.
If you are provided a Ztransform of a system without an ROC (i.e., an ambiguous x[n]) you can determine a unique x[n] provided you desire the following:
 Stability
 Causality
If you need stability then the ROC must contain the unit circle. If you need a causal system then the ROC must contain infinity and the system function will be a rightsided sequence. If you need an anticausal system then the ROC must contain the origin and the system function will be a leftsided sequence. If you need both, stability and causality, all the poles of the system function must be inside the unit circle.
The unique x[n] can then be found.
Properties
Time domain  Zdomain  Proof  ROC  

Notation  
Linearity  Contains ROC_{1} ∩ ROC_{2}  
Time expansion 
r: integer 

Decimation  ohiostate.edu or ee.ic.ac.uk  
Time shifting  ROC, except z = 0 if k > 0 and z = ∞ if k < 0  
Scaling in
the zdomain 

Time reversal  
Complex conjugation  
Real part  
Imaginary part  
Differentiation  
Convolution  Contains ROC_{1} ∩ ROC_{2}  
Crosscorrelation  Contains the intersection of ROC of and  
First difference  Contains the intersection of ROC of X_{1}(z) and z ≠ 0  
Accumulation  
Multiplication   
Initial value theorem: If xn causal, then
Final value theorem: If the poles of (z−1)X(z) are inside the unit circle, then
Table of common Ztransform pairs
Here:
is the unit (or Heaviside) step function and
is the discretetime (or Dirac delta) unit impulse function. Both are usually not considered as true functions but as distributions due to their discontinuity (their value on n = 0 usually does not really matter, except when working in discrete time, in which case they become degenerate discrete series ; in this section they are chosen to take the value 1 on n = 0, both for the continuous and discrete time domains, otherwise the content of the ROC column below would not apply). The two "functions" are chosen together so that the unit step function is the integral of the unit impulse function (in the continuous time domain), or the summation of the unit impulse function is the unit step function (in the discrete time domain), hence the choice of making their value on n = 0 fixed here to 1.
Signal,  Ztransform,  ROC  

1  1  all z  
2  
3  
4  
5  
6  
7  
8  
9  
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
20  
21 
Relationship to Fourier series and Fourier transform
For values of z in the region z=1, known as the unit circle, we can express the transform as a function of a single, real variable, ω, by defining z=e^{jω}. And the bilateral transform reduces to a Fourier series:

(
)
which is also known as the discretetime Fourier transform (DTFT) of the x[n] sequence. This 2πperiodic function is the periodic summation of a Fourier transform, which makes it a widely used analysis tool. To understand this, let X(f) be the Fourier transform of any function, x(t), whose samples at some interval, T, equal the x[n] sequence. Then the DTFT of the x[n] sequence can be written as:
When T has units of seconds, has units of hertz. Comparison of the two series reveals that is a normalized frequency with units of radians per sample. The value ω=2π corresponds to Hz. And now, with the substitution Eq.1 can be expressed in terms of the Fourier transform, X(•):
When sequence x(nT) represents the impulse response of an LTI system, these functions are also known as its frequency response. When the x(nT) sequence is periodic, its DTFT is divergent at one or more harmonic frequencies, and zero at all other frequencies. This is often represented by the use of amplitudevariant Dirac delta functions at the harmonic frequencies. Due to periodicity, there are only a finite number of unique amplitudes, which are readily computed by the much simpler discrete Fourier transform (DFT). (See DTFT; periodic data.)
Relationship to Laplace transform
Bilinear transform
The bilinear transform is a useful approximation for converting continuous time filters (represented in Laplace space) into discrete time filters (represented in z space), and vice versa. To do this, you can use the following substitutions in H(s) or H(z):
from Laplace to z (Tustin transformation), or
from z to Laplace. Through the bilinear transformation, the complex splane (of the Laplace transform) is mapped to the complex zplane (of the ztransform). While this mapping is (necessarily) nonlinear, it is useful in that it maps the entire jΩ axis of the splane onto the unit circle in the zplane. As such, the Fourier transform (which is the Laplace transform evaluated on the jΩ axis) becomes the discretetime Fourier transform. This assumes that the Fourier transform exists; i.e., that the jΩ axis is in the region of convergence of the Laplace transform.
Starred transform
Given a onesided Ztransform, X(z), of a timesampled function, the corresponding starred transform produces a Laplace transform and restores the dependence on sampling parameter, T:
The inverse Laplace transform is a mathematical abstraction known as an impulsesampled function.
Linear constantcoefficient difference equation
The linear constantcoefficient difference (LCCD) equation is a representation for a linear system based on the autoregressive movingaverage equation.
Both sides of the above equation can be divided by α_{0}, if it is not zero, normalizing α_{0} = 1 and the LCCD equation can be written
This form of the LCCD equation is favorable to make it more explicit that the "current" output y[n] is a function of past outputs y[n−p], current input x[n], and previous inputs x[n−q].
Transfer function
Taking the Ztransform of the above equation (using linearity and timeshifting laws) yields
and rearranging results in
Zeros and poles
From the fundamental theorem of algebra the numerator has M roots (corresponding to zeros of H) and the denominator has N roots (corresponding to poles). Rewriting the transfer function in terms of poles and zeros
where q_{k} is the kth zero and p_{k} is the kth pole. The zeros and poles are commonly complex and when plotted on the complex plane (zplane) it is called the polezero plot.
In addition, there may also exist zeros and poles at z = 0 and z = ∞. If we take these poles and zeros as well as multipleorder zeros and poles into consideration, the number of zeros and poles are always equal.
By factoring the denominator, partial fraction decomposition can be used, which can then be transformed back to the time domain. Doing so would result in the impulse response and the linear constant coefficient difference equation of the system.
Output response
If such a system H(z) is driven by a signal X(z) then the output is Y(z) = H(z)X(z). By performing partial fraction decomposition on Y(z) and then taking the inverse Ztransform the output y[n] can be found. In practice, it is often useful to fractionally decompose before multiplying that quantity by z to generate a form of Y(z) which has terms with easily computable inverse Ztransforms.
See also
 Advanced Ztransform
 Bilinear transform
 Difference equation (recurrence relation)
 Discrete convolution
 Discretetime Fourier transform
 Finite impulse response
 Formal power series
 Laplace transform
 Laurent series
 Probabilitygenerating function
 Star transform
 Zeta function regularization
References
 ^ E. R. Kanasewich (1981). Time sequence analysis in geophysics (3rd ed.). University of Alberta. pp. 185–186. ISBN 9780888640741.
 ^ J. R. Ragazzini and L. A. Zadeh (1952). "The analysis of sampleddata systems". Trans. Am. Inst. Elec. Eng. 71 (II): 225–234.
 ^ Cornelius T. Leondes (1996). Digital control systems implementation and computational techniques. Academic Press. p. 123. ISBN 9780120127795.
 ^ Eliahu Ibrahim Jury (1958). SampledData Control Systems. John Wiley & Sons.
 ^ Eliahu Ibrahim Jury (1973). Theory and Application of the ZTransform Method. Krieger Pub Co. ISBN 0882751220.
 ^ Eliahu Ibrahim Jury (1964). Theory and Application of the ZTransform Method. John Wiley & Sons. p. 1.
 ^ ^{a} ^{b} Enders A. Robinson, Sven Treitel (2008). Digital Imaging and Deconvolution: The ABCs of Seismic Exploration and Processing Digital Imaging and Deconvolution: The ABCs of Seismic Exploration and Processing. SEG Books. pp. 163, 375–376. ISBN 9781560801481.
 ^ ^{a} ^{b} E. R. Kanasewich (1981). Time Sequence Analysis in Geophysics. University of Alberta. pp. 186, 249. ISBN 9780888640741.
Further reading
 Refaat El Attar, Lecture notes on ZTransform, Lulu Press, Morrisville NC, 2005. ISBN 141161979X.
 Ogata, Katsuhiko, Discrete Time Control Systems 2nd Ed, PrenticeHall Inc, 1995, 1987. ISBN 0130342815.
 Alan V. Oppenheim and Ronald W. Schafer (1999). DiscreteTime Signal Processing, 2nd Edition, Prentice Hall Signal Processing Series. ISBN 0137549202.
External links
 Hazewinkel, Michiel, ed. (2001), "Ztransform", Encyclopedia of Mathematics, Springer, ISBN 9781556080104
 ZTransform table of some common Laplace transforms
 Mathworld's entry on the Ztransform
 ZTransform threads in Comp.DSP
 ZTransform Module by John H. Mathews
 A graphic of the relationship between Laplace transform splane to Zplane of the Z transform

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