Linear time-invariant system

 ( Digital Signal Processing ) 

In system analysis, among other fields of study, a linear time-invariant ( LTI) system is a system that produces an output signal from any input signal subject to the constraints of linearity and time-invariance; these terms are briefly defined in the overview below. These properties apply (exactly or approximately) to many important physical systems, in which case the response of the system to an arbitrary input can be found directly using convolution: where is called the system's impulse response and ∗ represents convolution (not to be confused with multiplication). What's more, there are systematic methods for solving any such system (determining ), whereas systems not meeting both properties are generally more difficult (or impossible) to solve analytically. A good example of an LTI system is any electrical circuit consisting of , , and .Hespanha 2009, p. 78.

Linear time-invariant system theory is also used in image processing, where the systems have spatial dimensions instead of, or in addition to, a temporal dimension. These systems may be referred to as linear translation-invariant to give the terminology the most general reach. In the case of generic discrete-time (i.e., sampled) systems, linear shift-invariant is the corresponding term. LTI system theory is an area of applied mathematics which has direct applications in electrical circuit analysis and design, signal processing and filter design, control theory, mechanical engineering, image processing, the design of measuring instruments of many sorts, NMR spectroscopy, and many other technical areas where systems of ordinary differential equations present themselves.

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Overview The defining properties of any LTI system are linearity and time invariance.

• Linearity means that the relationship between the input $x(t)$ and the output $y(t)$, both being regarded as functions, is a linear mapping: If $a$ is a constant then the system output to $ax(t)$ is $ay(t)$; if $x\text{'}(t)$ is a further input with system output $y\text{'}(t)$ then the output of the system to $x(t)+x\text{'}(t)$ is $y(t)+y\text{'}(t)$, this applying for all choices of $a$ , $x(t)$, $x\text{'}(t)$. The latter condition is often referred to as the superposition principle.
• Time invariance means that whether we apply an input to the system now or T seconds from now, the output will be identical except for a time delay of T seconds. That is, if the output due to input $x(t)$ is $y(t)$, then the output due to input $x(t-T)$ is $y(t-T)$. Hence, the system is time invariant because the output does not depend on the particular time the input is applied.
  (2025). 9780130412072, Prentice Hall. ISBN 9780130412072

Through these properties, it is reasoned that LTI systems can be characterized entirely by a single function called the system's impulse response, as, by superposition, any arbitrary signal can be expressed as a superposition of time-shifted Unit impulse. The output of the system $y(t)$ is simply the convolution of the input to the system $x(t)$ with the system's impulse response $h(t)$. This is called a continuous time system. Similarly, a discrete-time linear time-invariant (or, more generally, "shift-invariant") system is defined as one operating in discrete time: $y\_\{i\}\; =\; x\_\{i\}\; *\; h\_\{i\}$ where y, x, and h are sequences and the convolution, in discrete time, uses a discrete summation rather than an integral.

LTI systems can also be characterized in the frequency domain by the system's transfer function, which for a continuous-time or discrete-time system is the Laplace transform or Z-transform of the system's impulse response, respectively. As a result of the properties of these transforms, the output of the system in the frequency domain is the product of the transfer function and the corresponding frequency-domain representation of the input. In other words, convolution in the time domain is equivalent to multiplication in the frequency domain.

For all LTI systems, the , and the basis functions of the transforms, are complex number exponentials. As a result, if the input to a system is the complex waveform $A\_s\; e^\{st\}$ for some complex amplitude $A\_s$ and complex frequency $s$, the output will be some complex constant times the input, say $B\_s\; e^\{st\}$ for some new complex amplitude $B\_s$. The ratio $B\_s/A\_s$ is the transfer function at frequency $s$. The output signal will be shifted in phase and amplitude, but always with the same frequency upon reaching steady-state. LTI systems cannot produce frequency components that are not in the input.

LTI system theory is good at describing many important systems. Most LTI systems are considered "easy" to analyze, at least compared to the time-varying and/or nonlinear case. Any system that can be modeled as a linear differential equation with constant coefficients is an LTI system. Examples of such systems are electrical circuits made up of , , and (RLC circuit). Ideal spring–mass–damper systems are also LTI systems, and are mathematically equivalent to RLC circuits.

Most LTI system concepts are similar between the continuous-time and discrete-time cases. In image processing, the time variable is replaced with two space variables, and the notion of time invariance is replaced by two-dimensional shift invariance. When analyzing and MIMO systems, it is often useful to consider vectors of signals. A linear system that is not time-invariant can be solved using other approaches such as the Green function method.

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Continuous-time systems

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Impulse response and convolution The behavior of a linear, continuous-time, time-invariant system with input signal x( t) and output signal y( t) is described by the convolution integral:Crutchfield, p. 1. Welcome!

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$y(t)\; =\; (x\; *\; h)(t)$	$\backslash mathrel\{\backslash stackrel\{\backslash mathrm\{def\}\}\{=\}\}\; \backslash int\backslash limits\_\{-\backslash infty\}^\{\backslash infty\}\; x(t\; -\; \backslash tau)\backslash cdot\; h(\backslash tau)\; \backslash ,\; \backslash mathrm\{d\}\backslash tau$
	$=\; \backslash int\backslash limits\_\{-\backslash infty\}^\backslash infty\; x(\backslash tau)\backslash cdot\; h(t\; -\; \backslash tau)\; \backslash ,\backslash mathrm\{d\}\backslash tau,$ (using commutativity)

where $h(t)$ is the system's response to an impulse: $x(\backslash tau)\; =\; \backslash delta(\backslash tau)$. $y(t)$ is therefore proportional to a weighted average of the input function $x(\backslash tau)$. The weighting function is $h(-\backslash tau)$, simply shifted by amount $t$. As $t$ changes, the weighting function emphasizes different parts of the input function. When $h(\backslash tau)$ is zero for all negative $\backslash tau$, $y(t)$ depends only on values of $x$ prior to time $t$, and the system is said to be Causal system.

To understand why the convolution produces the output of an LTI system, let the notation $\backslash \{x(u-\backslash tau);\backslash \; u\backslash \}$ represent the function $x(u-\backslash tau)$ with variable $u$ and constant $\backslash tau$. And let the shorter notation $\backslash \{x\backslash \}$ represent $\backslash \{x(u);\backslash \; u\backslash \}$. Then a continuous-time system transforms an input function, $\backslash \{x\backslash \},$ into an output function, $\backslash \{y\backslash \}$. And in general, every value of the output can depend on every value of the input. This concept is represented by: $$y(t)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; O\_t\backslash \{x\backslash \},$$ where $O\_t$ is the transformation operator for time $t$. In a typical system, $y(t)$ depends most heavily on the values of $x$ that occurred near time $t$. Unless the transform itself changes with $t$, the output function is just constant, and the system is uninteresting.

For a linear system, $O$ must satisfy :

\, \mathrm{d}\tau.
     

And the time-invariance requirement is:

y(t - \tau)\\
                        &\mathrel{\stackrel{\text{def}}{=}} O_{t-\tau}\{x\}.\,
     

\end{align} |

In this notation, we can write the impulse response as $h(t)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; O\_t\backslash \{\backslash delta(u);\backslash \; u\backslash \}.$

Similarly:

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$h(t\; -\; \backslash tau)$	$\backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; O\_\{t-\backslash tau\}\backslash \{\backslash delta(u);\backslash \; u\backslash \}$
	$=\; O\_t\backslash \{\backslash delta(u\; -\; \backslash tau);\backslash \; u\backslash \}.$ (using )

Substituting this result into the convolution integral: $$\backslash begin\{align\}$$

 (x * h)(t) &= \int_{-\infty}^\infty x(\tau)\cdot h(t - \tau) \,\mathrm{d}\tau \\[4pt]
            &= \int_{-\infty}^\infty x(\tau)\cdot O_t\{\delta(u-\tau);\ u\} \, \mathrm{d}\tau,\,
     

\end{align}

which has the form of the right side of for the case $c\_\backslash tau\; =\; x(\backslash tau)$ and $x\_\backslash tau(u)\; =\; \backslash delta(u-\backslash tau).$

then allows this continuation:
     

$$\backslash begin\{align\}$$

 (x * h)(t) &= O_t\left\{\int_{-\infty}^\infty x(\tau)\cdot \delta(u-\tau) \, \mathrm{d}\tau;\ u \right\}\\[4pt]
            &= O_t\left\{x(u);\ u \right\}\\
            &\mathrel{\stackrel{\text{def}}{=}} y(t).\,
     

\end{align}

In summary, the input function, $\backslash \{x\backslash \}$, can be represented by a continuum of time-shifted impulse functions, combined "linearly", as shown at . The system's linearity property allows the system's response to be represented by the corresponding continuum of impulse responses, combined in the same way. And the time-invariance property allows that combination to be represented by the convolution integral.

The mathematical operations above have a simple graphical simulation.Crutchfield, p. 1. Exercises

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Exponentials as eigenfunctions

An eigenfunction is a function for which the output of the operator is a scaled version of the same function. That is, $$\backslash mathcal\{H\}f\; =\; \backslash lambda\; f,$$ where f is the eigenfunction and $\backslash lambda$ is the eigenvalue, a constant.

The exponential functions $A\; e^\{s\; t\}$, where $A,\; s\; \backslash in\; \backslash mathbb\{C\}$, are of a linear, time-invariant operator. A simple proof illustrates this concept. Suppose the input is $x(t)\; =\; A\; e^\{s\; t\}$. The output of the system with impulse response $h(t)$ is then $$\backslash int\_\{-\backslash infty\}^\backslash infty\; h(t\; -\; \backslash tau)\; A\; e^\{s\; \backslash tau\}\backslash ,\; \backslash mathrm\{d\}\; \backslash tau$$ which, by the commutative property of convolution, is equivalent to $$\backslash begin\{align\}$$

   \overbrace{\int_{-\infty}^\infty h(\tau) \, A e^{s (t - \tau)} \, \mathrm{d} \tau}^{\mathcal{H} f}
 &= \int_{-\infty}^\infty h(\tau) \, A e^{s t} e^{-s \tau} \, \mathrm{d} \tau \\[4pt]
 &= A e^{s t} \int_{-\infty}^{\infty} h(\tau) \, e^{-s \tau} \, \mathrm{d} \tau \\[4pt]
 &= \overbrace{\underbrace{A e^{s t}}_{\text{Input}}}^{f} \, \overbrace{\underbrace{H(s)}_{\text{Scalar}}}^{\lambda} \, , \\
     

\end{align}

where the scalar $$H(s)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash int\_\{-\backslash infty\}^\backslash infty\; h(t)\; e^\{-s\; t\}\; \backslash ,\; \backslash mathrm\{d\}\; t$$ is dependent only on the parameter s.

So the system's response is a scaled version of the input. In particular, for any $A,\; s\; \backslash in\; \backslash mathbb\{C\}$, the system output is the product of the input $A\; e^\{st\}$ and the constant $H(s)$. Hence, $A\; e^\{s\; t\}$ is an eigenfunction of an LTI system, and the corresponding eigenvalue is $H(s)$.

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Direct proof

It is also possible to directly derive complex exponentials as eigenfunctions of LTI systems.

Let's set $v(t)\; =\; e^\{i\; \backslash omega\; t\}$ some complex exponential and $v\_a(t)\; =\; e^\{i\; \backslash omega\; (t+a)\}$ a time-shifted version of it.

$Hv\_a(t)\; =\; e^\{i\backslash omega\; a\}\; Hv(t)$ by linearity with respect to the constant $e^\{i\; \backslash omega\; a\}$.

$Hv\_a(t)\; =\; Hv(t+a)$ by time invariance of $H$.

So $Hv(t+a)\; =\; e^\{i\; \backslash omega\; a\}\; Hv(t)$. Setting $t\; =\; 0$ and renaming we get: $$Hv(\backslash tau)\; =\; e^\{i\backslash omega\; \backslash tau\}\; Hv(0)$$ i.e. that a complex exponential $e^\{i\; \backslash omega\; \backslash tau\}$ as input will give a complex exponential of same frequency as output.

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Fourier and Laplace transforms

The eigenfunction property of exponentials is very useful for both analysis and insight into LTI systems. The one-sided Laplace transform $$H(s)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash mathcal\{L\}\backslash \{h(t)\backslash \}\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash int\_0^\backslash infty\; h(t)\; e^\{-s\; t\}\; \backslash ,\; \backslash mathrm\{d\}\; t$$ is exactly the way to get the eigenvalues from the impulse response. Of particular interest are pure sinusoids (i.e., exponential functions of the form $e^\{j\; \backslash omega\; t\}$ where $\backslash omega\; \backslash in\; \backslash mathbb\{R\}$ and $j\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash sqrt\{-1\}$). The Fourier transform $H(j\; \backslash omega)\; =\; \backslash mathcal\{F\}\backslash \{h(t)\backslash \}$ gives the eigenvalues for pure complex sinusoids. Both of $H(s)$ and $H(j\backslash omega)$ are called the system function, system response, or transfer function.

The Laplace transform is usually used in the context of one-sided signals, i.e. signals that are zero for all values of t less than some value. Usually, this "start time" is set to zero, for convenience and without loss of generality, with the transform integral being taken from zero to infinity (the transform shown above with lower limit of integration of negative infinity is formally known as the bilateral Laplace transform).

The Fourier transform is used for analyzing systems that process signals that are infinite in extent, such as modulated sinusoids, even though it cannot be directly applied to input and output signals that are not square integrable. The Laplace transform actually works directly for these signals if they are zero before a start time, even if they are not square integrable, for stable systems. The Fourier transform is often applied to spectra of infinite signals via the Wiener–Khinchin theorem even when Fourier transforms of the signals do not exist.

Due to the convolution property of both of these transforms, the convolution that gives the output of the system can be transformed to a multiplication in the transform domain, given signals for which the transforms exist $$y(t)\; =\; (h*x)(t)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash int\_\{-\backslash infty\}^\backslash infty\; h(t\; -\; \backslash tau)\; x(\backslash tau)\; \backslash ,\; \backslash mathrm\{d\}\; \backslash tau\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash mathcal\{L\}^\{-1\}\backslash \{H(s)X(s)\backslash \}.$$

One can use the system response directly to determine how any particular frequency component is handled by a system with that Laplace transform. If we evaluate the system response (Laplace transform of the impulse response) at complex frequency , where , we obtain | H( s)| which is the system gain for frequency f. The relative phase shift between the output and input for that frequency component is likewise given by arg( H( s)).

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Examples

{\mathrm{d}t} \left( c_1 x_1(t) + c_2 x_2(t) \right) = c_1 x'_1(t) + c_2 x'_2(t)   (i.e., it is linear)

• $\backslash frac\{\backslash mathrm\{d\}\}\{\backslash mathrm\{d\}t\}\; x(t-\backslash tau)\; =\; x\text{'}(t-\backslash tau)$   (i.e., it is time invariant)

When the Laplace transform of the derivative is taken, it transforms to a simple multiplication by the Laplace variable s. $$\backslash mathcal\{L\}\backslash left\backslash \{\backslash frac\{\backslash mathrm\{d\}\}\{\backslash mathrm\{d\}t\}x(t)\backslash right\backslash \}\; =\; s\; X(s)$$ That the derivative has such a simple Laplace transform partly explains the utility of the transform. | Another simple LTI operator is an averaging operator $$\backslash mathcal\{A\}\backslash left\backslash \{x(t)\backslash right\backslash \}\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash int\_\{t-a\}^\{t+a\}\; x(\backslash lambda)\; \backslash ,\; \backslash mathrm\{d\}\; \backslash lambda.$$

By the linearity of integration, $$\backslash begin\{align\}$$

 \mathcal{A} \{c_1 x_1(t) + c_2 x_2(t)\}
   &= \int_{t-a}^{t+a} ( c_1 x_1(\lambda) + c_2 x_2(\lambda)) \, \mathrm{d} \lambda\\
   &= c_1 \int_{t-a}^{t+a} x_1(\lambda) \, \mathrm{d} \lambda + c_2 \int_{t-a}^{t+a} x_2(\lambda) \, \mathrm{d} \lambda\\
   &= c_1 \mathcal{A}\{x_1(t)\} + c_2 \mathcal{A} \{x_2(t) \},
     

\end{align}

it is linear. Additionally, because $$\backslash begin\{align\}$$

 \mathcal{A}\left\{x(t-\tau)\right\}
   &= \int_{t-a}^{t+a} x(\lambda-\tau) \, \mathrm{d} \lambda\\
   &= \int_{(t-\tau)-a}^{(t-\tau)+a} x(\xi) \, \mathrm{d} \xi\\
   &= \mathcal{A}\{x\}(t-\tau),
     

\end{align}

it is time invariant. In fact, $\backslash mathcal\{A\}$ can be written as a convolution with the boxcar function $\backslash Pi(t)$. That is, $$\backslash mathcal\{A\}\backslash left\backslash \{x(t)\backslash right\backslash \}\; =\; \backslash int\_\{-\backslash infty\}^\backslash infty\; \backslash Pi\backslash left(\backslash frac\{\backslash lambda-t\}\{2a\}\backslash right)\; x(\backslash lambda)\; \backslash ,\; \backslash mathrm\{d\}\; \backslash lambda,$$

where the boxcar function $$\backslash Pi(t)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash begin\{cases\}$$

   1 &\text{if } |t| < \frac{1}{2},\\
   0 &\text{if } |t| > \frac{1}{2}.
 \end{cases}
     

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Important system properties

Some of the most important properties of a system are causality and stability. Causality is a necessity for a physical system whose independent variable is time, however this restriction is not present in other cases such as image processing.

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Causality

A system is causal if the output depends only on present and past, but not future inputs. A necessary and sufficient condition for causality is

where $h(t)$ is the impulse response. It is not possible in general to determine causality from the two-sided Laplace transform. However, when working in the time domain, one normally uses the one-sided Laplace transform which requires causality.

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Stability

A system is bounded-input, bounded-output stable (BIBO stable) if, for every bounded input, the output is finite. Mathematically, if every input satisfying

leads to an output satisfying

(that is, a finite Infinity norm of $x(t)$ implies a finite maximum absolute value of $y(t)$), then the system is stable. A necessary and sufficient condition is that $h(t)$, the impulse response, is in Lp space (has a finite L¹ norm):

In the frequency domain, the region of convergence must contain the imaginary axis $s\; =\; j\backslash omega$.

As an example, the ideal low-pass filter with impulse response equal to a sinc function is not BIBO stable, because the sinc function does not have a finite L¹ norm. Thus, for some bounded input, the output of the ideal low-pass filter is unbounded. In particular, if the input is zero for and equal to a sinusoid at the cut-off frequency for $t\; >\; 0$, then the output will be unbounded for all times other than the zero crossings.

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Discrete-time systems

Almost everything in continuous-time systems has a counterpart in discrete-time systems.

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Discrete-time systems from continuous-time systems

In many contexts, a discrete time (DT) system is really part of a larger continuous time (CT) system. For example, a digital recording system takes an analog sound, digitizes it, possibly processes the digital signals, and plays back an analog sound for people to listen to.

In practical systems, DT signals obtained are usually uniformly sampled versions of CT signals. If $x(t)$ is a CT signal, then the sampling circuit used before an analog-to-digital converter will transform it to a DT signal: $$x\_n\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; x(nT)\; \backslash qquad\; \backslash forall\; \backslash ,\; n\; \backslash in\; \backslash mathbb\{Z\},$$ where T is the sampling period. Before sampling, the input signal is normally run through a so-called Nyquist filter which removes frequencies above the "folding frequency" 1/(2T); this guarantees that no information in the filtered signal will be lost. Without filtering, any frequency component above the folding frequency (or Nyquist frequency) is Aliasing to a different frequency (thus distorting the original signal), since a DT signal can only support frequency components lower than the folding frequency.

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Impulse response and convolution

Let $\backslash \{xm;\backslash \; m\backslash \}$ represent the sequence $\backslash \{xm;\backslash text\{\; for\; all\; integer\; values\; of\; \}\; m\backslash \}.$

And let the shorter notation $\backslash \{x\backslash \}$ represent $\backslash \{xm;\backslash \; m\backslash \}.$

A discrete system transforms an input sequence, $\backslash \{x\backslash \}$ into an output sequence, $\backslash \{y\backslash \}.$ In general, every element of the output can depend on every element of the input. Representing the transformation operator by $O$, we can write: $$yn\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; O\_n\backslash \{x\backslash \}.$$

Note that unless the transform itself changes with n, the output sequence is just constant, and the system is uninteresting. (Thus the subscript, n.) In a typical system, y n depends most heavily on the elements of x whose indices are near n.

For the special case of the Kronecker delta function, $xm\; =\; \backslash deltam,$ the output sequence is the impulse response: $$hn\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; O\_n\backslash \{\backslash deltam;\backslash \; m\backslash \}.$$

For a linear system, $O$ must satisfy:

And the time-invariance requirement is:

y[n-k]\\
                   &\mathrel{\stackrel{\text{def}}{=}} O_{n-k}\{x\}.\,
     

\end{align}

In such a system, the impulse response, $\backslash \{h\backslash \}$, characterizes the system completely. That is, for any input sequence, the output sequence can be calculated in terms of the input and the impulse response. To see how that is done, consider the identity: $$xm\; \backslash equiv\; \backslash sum\_\{k=-\backslash infty\}^\{\backslash infty\}\; xk\; \backslash cdot\; \backslash deltam,$$

which expresses $\backslash \{x\backslash \}$ in terms of a sum of weighted delta functions.

Therefore: $$\backslash begin\{align\}$$

 y[n] = O_n\{x\}
   &= O_n\left\{\sum_{k=-\infty}^\infty x[k]\cdot \delta[m-k];\ m \right\}\\
   &= \sum_{k=-\infty}^\infty x[k]\cdot O_n\{\delta[m-k];\ m\},\,
     

\end{align}

where we have invoked for the case $c\_k\; =\; xk$ and $x\_km\; =\; \backslash deltam-k$.

And because of , we may write: $$\backslash begin\{align\}$$

 O_n\{\delta[m-k];\ m\} &\mathrel{\stackrel{\quad}{=}} O_{n-k}\{\delta[m];\ m\} \\
                        &\mathrel{\stackrel{\text{def}}{=}} h[n-k].
     

\end{align}

Therefore:

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$yn$	$=\; \backslash sum\_\{k=-\backslash infty\}^\{\backslash infty\}\; xk\; \backslash cdot\; hn$
	$=\; \backslash sum\_\{k=-\backslash infty\}^\{\backslash infty\}\; xn-k\; \backslash cdot\; hk,$ (commutativity)

which is the familiar discrete convolution formula. The operator $O\_n$ can therefore be interpreted as proportional to a weighted average of the function x k. The weighting function is h− k, simply shifted by amount n. As n changes, the weighting function emphasizes different parts of the input function. Equivalently, the system's response to an impulse at n=0 is a "time" reversed copy of the unshifted weighting function. When h k is zero for all negative k, the system is said to be Causal system.

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Exponentials as eigenfunctions

An eigenfunction is a function for which the output of the operator is the same function, scaled by some constant. In symbols, $$\backslash mathcal\{H\}f\; =\; \backslash lambda\; f\; ,$$

where f is the eigenfunction and $\backslash lambda$ is the eigenvalue, a constant.

The exponential functions $z^n\; =\; e^\{sT\; n\}$, where $n\; \backslash in\; \backslash mathbb\{Z\}$, are of a linear, time-invariant operator. $T\; \backslash in\; \backslash mathbb\{R\}$ is the sampling interval, and $z\; =\; e^\{sT\},\; \backslash \; z,s\; \backslash in\; \backslash mathbb\{C\}$. A simple proof illustrates this concept.

Suppose the input is $xn\; =\; z^n$. The output of the system with impulse response $hn$ is then $$\backslash sum\_\{m=-\backslash infty\}^\{\backslash infty\}\; hn-m\; \backslash ,\; z^m$$

which is equivalent to the following by the commutative property of convolution $$\backslash sum\_\{m=-\backslash infty\}^\{\backslash infty\}\; hm\; \backslash ,\; z^\{(n\; -\; m)\}\; =\; z^n\; \backslash sum\_\{m=-\backslash infty\}^\{\backslash infty\}\; hm\; \backslash ,\; z^\{-m\}\; =\; z^n\; H(z)$$ where $$H(z)\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash sum\_\{m=-\backslash infty\}^\backslash infty\; hm\; z^\{-m\}$$ is dependent only on the parameter z.

So $z^n$ is an eigenfunction of an LTI system because the system response is the same as the input times the constant $H(z)$.

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Z and discrete-time Fourier transforms

The eigenfunction property of exponentials is very useful for both analysis and insight into LTI systems. The Z transform $$H(z)\; =\; \backslash mathcal\{Z\}\backslash \{hn\backslash \}\; =\; \backslash sum\_\{n=-\backslash infty\}^\backslash infty\; hn\; z^\{-n\}$$

is exactly the way to get the eigenvalues from the impulse response. Of particular interest are pure sinusoids; i.e. exponentials of the form $e^\{j\; \backslash omega\; n\}$, where $\backslash omega\; \backslash in\; \backslash mathbb\{R\}$. These can also be written as $z^n$ with $z\; =\; e^\{j\; \backslash omega\}$. The discrete-time Fourier transform (DTFT) $H(e^\{j\; \backslash omega\})\; =\; \backslash mathcal\{F\}\backslash \{hn\backslash \}$ gives the eigenvalues of pure sinusoids. Both of $H(z)$ and $H(e^\{j\backslash omega\})$ are called the system function, system response, or transfer function.

Like the one-sided Laplace transform, the Z transform is usually used in the context of one-sided signals, i.e. signals that are zero for t<0. The discrete-time Fourier transform Fourier series may be used for analyzing periodic signals.

Due to the convolution property of both of these transforms, the convolution that gives the output of the system can be transformed to a multiplication in the transform domain. That is, $$yn\; =\; (h*x)n\; =\; \backslash sum\_\{m=-\backslash infty\}^\backslash infty\; hn-m\; xm\; =\; \backslash mathcal\{Z\}^\{-1\}\backslash \{H(z)X(z)\backslash \}.$$

Just as with the Laplace transform transfer function in continuous-time system analysis, the Z transform makes it easier to analyze systems and gain insight into their behavior.

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Examples

{=}} xn-1.

• $D\; \backslash left(\; c\_1\; \backslash cdot\; x\_1n\; +\; c\_2\; \backslash cdot\; x\_2n\; \backslash right)\; =\; c\_1\; \backslash cdot\; x\_1n\; +\; c\_2\backslash cdot\; x\_2n\; =\; c\_1\backslash cdot\; Dx\_1n\; +\; c\_2\backslash cdot\; Dx\_2n$   (i.e., it is linear)
• $D\backslash \{xn\backslash \}\; =\; xn\; =\; x(n\; =\; D\backslash \{x\backslash \}n$   (i.e., it is time invariant)

The Z transform of the delay operator is a simple multiplication by z⁻¹. That is, $$\backslash mathcal\{Z\}\backslash left\backslash \{Dxn\backslash right\backslash \}\; =\; z^\{-1\}\; X(z).$$ | Another simple LTI operator is the averaging operator $$\backslash mathcal\{A\}\backslash left\backslash \{xn\backslash right\backslash \}\; \backslash mathrel\{\backslash stackrel\{\backslash text\{def\}\}\{=\}\}\; \backslash sum\_\{k=n-a\}^\{n+a\}\; xk.$$

Because of the linearity of sums, $$\backslash begin\{align\}$$

 \mathcal{A}\left\{c_1 x_1[n] + c_2 x_2[n] \right\}
   &= \sum_{k=n-a}^{n+a} \left( c_1 x_1[k] + c_2 x_2[k] \right)\\
   &= c_1 \sum_{k=n-a}^{n+a} x_1[k] + c_2 \sum_{k=n-a}^{n+a} x_2[k]\\
   &= c_1 \mathcal{A}\left\{x_1[n] \right\} + c_2 \mathcal{A}\left\{x_2[n] \right\},
     

\end{align}

and so it is linear. Because, $$\backslash begin\{align\}$$

 \mathcal{A}\left\{x[n-m]\right\}
   &= \sum_{k=n-a}^{n+a} x[k-m]\\
   &= \sum_{k'=(n-m)-a}^{(n-m)+a} x[k']\\
   &= \mathcal{A}\left\{x\right\}[n-m],
     

\end{align}

it is also time invariant.

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Important system properties

The input-output characteristics of discrete-time LTI system are completely described by its impulse response $hn$. Two of the most important properties of a system are causality and stability. Non-causal (in time) systems can be defined and analyzed as above, but cannot be realized in real-time. Unstable systems can also be analyzed and built, but are only useful as part of a larger system whose overall transfer function is stable.

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Causality

A discrete-time LTI system is causal if the current value of the output depends on only the current value and past values of the input.Phillips 2007, p. 508. A necessary and sufficient condition for causality is where $hn$ is the impulse response. It is not possible in general to determine causality from the Z transform, because the inverse transform is not unique. When a region of convergence is specified, then causality can be determined.

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Stability

A system is bounded input, bounded output stable (BIBO stable) if, for every bounded input, the output is finite. Mathematically, if

implies that

(that is, if bounded input implies bounded output, in the sense that the Infinity norm of $xn$ and $yn$ are finite), then the system is stable. A necessary and sufficient condition is that $hn$, the impulse response, satisfies

In the frequency domain, the region of convergence must contain the unit circle (i.e., the locus satisfying $|z|\; =\; 1$ for complex z).

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Notes

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See also

• Circulant matrix
• Frequency response
• Impulse response
• System analysis
• Green function
• Signal-flow graph

• Phillips, C.L., Parr, J.M., & Eve Riskin (2025). 9780130412072, Prentice Hall. ISBN 9780130412072
• Hespanha, J.P. (2025). 9780691140216, Princeton university press. ISBN 9780691140216

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Further reading

• Boaz Porat (1997). 9780471149613, John Wiley. ISBN 9780471149613

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External links

• ECE 209: Review of Circuits as LTI Systems – Short primer on the mathematical analysis of (electrical) LTI systems.
• ECE 209: Sources of Phase Shift – Gives an intuitive explanation of the source of phase shift in two common electrical LTI systems.
• JHU 520.214 Signals and Systems course notes. An encapsulated course on LTI system theory. Adequate for self teaching.
• LTI system example: RC low-pass filter. Amplitude and phase response.

Categories: Digital Signal Processing, Electrical Engineering, Classical Control Theory, Signal Processing, Frequency-domain Analysis, Time Domain Analysis

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