Causal filter

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In signal processing, a causal filter is one whose output depends only on past and present inputs. A filter whose output also depends on future inputs is non-causal. Filters that operate in real time are causal. In effect that means the output sample that best represents the input at time $t\,$ comes out slightly later. A common design practice is to create a realizable filter by shortening and/or time-shifting a non-causal impulse response. If shortening is necessary, it is often accomplished as the product of the impulse-response with a window function.

[edit] Example

The following definition is a moving (or "sliding") average of input data $s(x)\,$ . A constant factor of 1/2 is omitted for simplicity:

$f(x) = \int_{x-1}^{x+1} s(\tau)\, d\tau\ = \int_{-1}^{+1} s(x + \tau) \,d\tau\,$

where x could represent a spatial coordinate, as in image processing. But if $x\,$ represents time $(t)\,$ , then a moving average defined that way is non-causal (also called non-realizable), because $f(t)\,$ depends on future inputs, such as $s(t+1)\,$ . A realizable output is

$f(t-1) = \int_{-2}^{0} s(t + \tau)\, d\tau = \int_{0}^{+2} s(t - \tau) \, d\tau\,$

which is a delayed version of the non-realizable output.

Any linear filter (such as a moving average) can be characterized by a function h(t) called its impulse response. Its output is the convolution

$f(t) = (h*s)(t) = \int_{-\infty}^{\infty} h(\tau) s(t - \tau)\, d\tau. \,$

In those terms, causality requires

$f(t) = \int_{0}^{\infty} h(\tau) s(t - \tau)\, d\tau$

and general equality of these two expressions requires h(t) = 0 for all t < 0.

[edit] Characterization of causal filters in the frequency domain

Let h(t) be a causal filter with corresponding Fourier transform H(ω). Define the function

$g(t) = {h(t) + h^{*}(-t) \over 2}$

which is non-causal. On the other hand, g(t) is Hermitian and, consequently, its Fourier transform G(ω) is real-valued. We now have the following relation

$h(t) = 2\, \operatorname{step}(t) \cdot g(t)\,$