Matched Z-transform method

The matched Z-transform method, also called the pole–zero mapping^[1]^[2] or pole–zero matching method,^[3] and abbreviated MPZ or MZT,^[4] is a technique for converting a continuous-time filter design to a discrete-time filter (digital filter) design.

The method works by mapping all poles and zeros of the s-plane design to z-plane locations $z=e^{sT}$ , for a sample interval $T=1/f_{\mathrm {s} }$ .^[5] So an analog filter with transfer function:

H(s)=k_{\mathrm {a} }{\frac {\prod _{i=1}^{M}(s-\xi _{i})}{\prod _{i=1}^{N}(s-p_{i})}}

is transformed into the digital transfer function

H(z)=k_{\mathrm {d} }{\frac {\prod _{i=1}^{M}(1-e^{\xi _{i}T}z^{-1})}{\prod _{i=1}^{N}(1-e^{p_{i}T}z^{-1})}}

The gain $k_{\mathrm {d} }$ must be adjusted to normalize the desired gain, typically set to match the analog filter's gain at DC by setting $s=0$ and $z=1$ and solving for $k_{\mathrm {d} }$ .^[3]^[6]

Since the mapping wraps the s-plane's $j\omega$ axis around the z-plane's unit circle repeatedly, any zeros (or poles) greater than the Nyquist frequency will be mapped to an aliased location.^[7]

In the (common) case that the analog transfer function has more poles than zeros, the zeros at $s=\infty$ may optionally be shifted down to the Nyquist frequency by putting them at $z=-1$ , causing the transfer function to drop off as $z\rightarrow -1$ in much the same manner as with the bilinear transform (BLT).^[1]^[3]^[6]^[7]

While this transform preserves stability and minimum phase, it preserves neither time- nor frequency-domain response and so is not widely used.^[8]^[7] More common methods include the BLT and impulse invariance methods.^[4] MZT does provide less high frequency response error than the BLT, however, making it easier to correct by adding additional zeros, which is called the MZTi (for "improved").^[9]

A specific application of the matched Z-transform method in the digital control field is with the Ackermann's formula, which changes the poles of the controllable system; in general from an unstable (or nearby) location to a stable location.

References

^ ^a ^b Won Young Yang (2009). Signals and Systems with MATLAB. Springer. p. 292. ISBN 978-3-540-92953-6.
^ Bong Wie (1998). Space vehicle dynamics and control. AIAA. p. 151. ISBN 978-1-56347-261-9.
^ ^a ^b ^c Arthur G. O. Mutambara (1999). Design and analysis of control systems. CRC Press. p. 652. ISBN 978-0-8493-1898-6.
^ ^a ^b Al-Alaoui, M. A. (February 2007). "Novel Approach to Analog-to-Digital Transforms". IEEE Transactions on Circuits and Systems I: Regular Papers. 54 (2): 338–350. doi:10.1109/tcsi.2006.885982. ISSN 1549-8328. S2CID 9049852.
^ S. V. Narasimhan and S. Veena (2005). Signal processing: principles and implementation. Alpha Science Int'l Ltd. p. 260. ISBN 978-1-84265-199-5.
^ ^a ^b Franklin, Gene F. (2015). Feedback control of dynamic systems. Powell, J. David, Emami-Naeini, Abbas (Seventh ed.). Boston: Pearson. pp. 607–611. ISBN 978-0133496598. OCLC 869825370. Because physical systems often have more poles than zeros, it is useful to arbitrarily add zeros at z = -1.
^ ^a ^b ^c Rabiner, Lawrence R; Gold, Bernard (1975). Theory and application of digital signal processing. Englewood Cliffs, New Jersey: Prentice-Hall. pp. 224–226. ISBN 0139141014. The expediency of artificially adding zeros at z = —1 to the digital system has been suggested ... but this ad hoc technique is at best only a stopgap measure. ... In general, use of impulse invariant or bilinear transformation is to be preferred over the matched z transformation.
^ Jackson, Leland B. (1996). Digital Filters and Signal Processing. Springer Science & Business Media. p. 262. ISBN 9780792395591. although perfectly usable filters can be designed in this way, no special time- or frequency-domain properties are preserved by this transformation, and it is not widely used.
^ Ojas, Chauhan; David, Gunness (2007-09-01). "Optimizing the Magnitude Response of Matched Z-Transform Filters ("MZTi") for Loudspeaker Equalization". Audio Engineering Society. Archived from the original on July 27, 2019. Alt URL

[:3-1] Won Young Yang (2009). Signals and Systems with MATLAB. Springer. p. 292. ISBN 978-3-540-92953-6.

[2] Bong Wie (1998). Space vehicle dynamics and control. AIAA. p. 151. ISBN 978-1-56347-261-9.

[:1-3] Arthur G. O. Mutambara (1999). Design and analysis of control systems. CRC Press. p. 652. ISBN 978-0-8493-1898-6.

[:4-4] Al-Alaoui, M. A. (February 2007). "Novel Approach to Analog-to-Digital Transforms". IEEE Transactions on Circuits and Systems I: Regular Papers. 54 (2): 338–350. doi:10.1109/tcsi.2006.885982. ISSN 1549-8328. S2CID 9049852.

[5] S. V. Narasimhan and S. Veena (2005). Signal processing: principles and implementation. Alpha Science Int'l Ltd. p. 260. ISBN 978-1-84265-199-5.

[:2-6] Franklin, Gene F. (2015). Feedback control of dynamic systems. Powell, J. David, Emami-Naeini, Abbas (Seventh ed.). Boston: Pearson. pp. 607–611. ISBN 978-0133496598. OCLC 869825370. Because physical systems often have more poles than zeros, it is useful to arbitrarily add zeros at z = -1.

[:0-7] Rabiner, Lawrence R; Gold, Bernard (1975). Theory and application of digital signal processing. Englewood Cliffs, New Jersey: Prentice-Hall. pp. 224–226. ISBN 0139141014. The expediency of artificially adding zeros at z = —1 to the digital system has been suggested ... but this ad hoc technique is at best only a stopgap measure. ... In general, use of impulse invariant or bilinear transformation is to be preferred over the matched z transformation.

[8] Jackson, Leland B. (1996). Digital Filters and Signal Processing. Springer Science & Business Media. p. 262. ISBN 9780792395591. although perfectly usable filters can be designed in this way, no special time- or frequency-domain properties are preserved by this transformation, and it is not widely used.

[9] Ojas, Chauhan; David, Gunness (2007-09-01). "Optimizing the Magnitude Response of Matched Z-Transform Filters ("MZTi") for Loudspeaker Equalization". Audio Engineering Society. Archived from the original on July 27, 2019. Alt URL

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

v t e Digital signal processing
Theory	Detection theory Discrete signal Estimation theory Nyquist–Shannon sampling theorem
Sub-fields	Audio signal processing Digital image processing Speech processing Statistical signal processing
Techniques	Z-transform Advanced z-transform Matched Z-transform method Bilinear transform Constant-Q transform Discrete cosine transform (DCT) Discrete Fourier transform (DFT) Discrete-time Fourier transform (DTFT) Impulse invariance Integral transform Laplace transform Post's inversion formula Starred transform Zak transform
Sampling	Aliasing Anti-aliasing filter Downsampling Nyquist rate / frequency Oversampling Quantization Sampling rate Undersampling Upsampling