page_title: Linear time-invariant system
section_title: Overview
hierarchical_section_title: Linear time-invariant system / Overview
context_page_description: In signal analysis and other fields of study, a linear time-invariant system is a system that meets the following criteria: 1 the output signal is proportional to the input signal; 2 the scale of the proportion doesn't vary throughout the range of inputs under analysis; 3 the relationship between the input and output doesn't vary over time; and 4 in the case of multiple input signals, the addition operation on the inputs is preserved in the output. Linear time-invariance also finds application in image processing and field theory, where the LTI 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 systems, linear shift-invariant is the corresponding term. A good example of LTI systems are electrical circuits that can be made up of resistors, capacitors, and inductors.
  It has been used in applied mathematics and has direct applications in NMR spectroscopy, seismology, circuits, signal processing, control theory, and other technical areas.
context_section_description: The defining properties of any LTI system are linearity and time invariance.
  Linearity means that the relationship between the input and the output of the system is a linear map: If input  produces response  and input  produces response  then the scaled and summed input  produces the scaled and summed response  where  and  are complex scalars. It follows that this can be extended to an arbitrary number of terms, and so for real numbers ,
  Input      produces output   
  In particular,
  where  and  are scalars and inputs that vary over a continuum indexed by .  Thus if an input function can be represented by a continuum of input functions, combined "linearly", as shown, then the corresponding output function can be represented by the corresponding continuum of output functions, scaled and summed in the same way.
  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  is , then the output due to input  is . Hence, the system is time invariant because the output does not depend on the particular time the input is applied.
  The fundamental result in LTI system theory is that any LTI system can be characterized entirely by a single function called the system's impulse response.  The output of the system is simply the convolution of the input to the system with the system's impulse response.  This method of analysis is often called the time domain point-of-view.  The same result is true of discrete-time linear shift-invariant systems in which signals are discrete-time samples, and convolution is defined on sequences.
  Equivalently, any LTI system can be characterized in the frequency domain by the system's transfer function, which is the Laplace transform of the system's impulse response (or Z transform in the case of discrete-time systems).  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 transform of the input. In other words, convolution in the time domain is equivalent to multiplication in the frequency domain.
  For all LTI systems, the eigenfunctions, and the basis functions of the transforms, are complex exponentials.  This is, if the input to a system is the complex waveform  for some complex amplitude  and complex frequency , the output will be some complex constant times the input, say  for some new complex amplitude .  The ratio  is the transfer function at frequency .
  Since sinusoids are a sum of complex exponentials with complex-conjugate frequencies, if the input to the system is a sinusoid, then the output of the system will also be a sinusoid, perhaps with a different amplitude and a different phase, 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 resistors, inductors, and capacitors (RLC circuits).  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 (linear shift-invariant) 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 filter banks 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. The same method must be used when the initial conditions of the problem are not null.
caption: Relationship between the time domain and the frequency domain
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