Causal system

Causal system

A causal system (also known as a physical or nonanticipative system) is a system where the output depends on past/current inputs but not future inputs i.e. the output y(t0) only depends on the input x(t) for values of t \le t_{0}.

The idea that the output of a function at any time depends only on past and present values of input is defined by the property commonly referred to as causality. A system that has some dependence on input values from the future (in addition to possible dependence on past or current input values) is termed a non-causal or acausal system, and a system that depends solely on future input values is an anticausal system. Note that some authors have defined an anticausal system as one that depends solely on future and present input values or, more simply, as a system that does not depend on past input values.

Classically, nature or physical reality has been considered to be a causal system. Physics involving special relativity or general relativity require more careful definitions of causality, as described elaborately in causality (physics).

The causality of systems also plays an important role in digital signal processing, where filters are often constructed so that they are causal. For more information, see causal filter. A causal system is said to be highly stable system as outputs only depend on past history of the system and not on any future input, which means, the system is not vulnerable to future failures. For a causal system, the impulse response of the system must be 0 for all t<0. That is the sole necessary as well as sufficient condition for causality of a system, linear or non-linear.

Note that the systems may be discrete or continuous. Similar rules apply to both kind of systems.

Contents

Mathematical definitions

Definition 1: A system mapping x to y is causal if and only if, for any pair of input signals x1(t) and x2(t) such that

x_{1}(t) = x_{2}(t), \quad \forall \ t \le t_{0},

the corresponding outputs satisfy

y_{1}(t) = y_{2}(t), \quad \forall \ t \le t_{0}.

Definition 2: Suppose h(t) is the impulse response of the system H. (only fully accurate for a system described by linear constant coefficient differential equation)

h(t) = 0, \quad \forall \ t <0

then the system H is causal, otherwise it is non-causal.

Examples

The following examples are for systems with an input x and output y.

Examples of causal systems

  • Memoryless system
y \left( t \right) = 1 + x \left( t \right) \cos \left( \omega t \right)
  • Autoregressive filter
y \left( t \right) = \int_0^\infty x(t-\tau) e^{-\beta\tau}\,d\tau

Examples of non-causal (acausal) systems

y(t)=\int_{-\infty}^\infty \sin (t+\tau) x(\tau)\,d\tau
  • Central moving average
y_n=\frac{1}{2}\,x_{n-1}+\frac{1}{2}\,x_{n+1}
  • For coefficients of t
y \left( t \right) =x(at)

Examples of anti-causal systems

y(t) =\int _0^\infty \sin (t+\tau) x(\tau)\,d\tau
y \left( t \right) =x(-t)
  • Look-ahead
yn = xn + 1

References

  • Oppenheim, Alan V.; Willsky, Alan S.; Nawab, Hamid; with S. Hamid (1998). Signals and Systems. Pearson Education. ISBN 0-13-814757-4. 

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