## Showing Linearity and Time Invariance, or Not

The filter is nonlinear and time invariant. The scaling property of linearity clearly fails since, scaling by gives the output signal , while . The filter is time invariant, however, because delaying by samples gives which is the same as .

The filter is linear and time varying. We can show linearity by setting the input to a linear combination of two signals , where and are constants:

Thus, scaling and superposition are verified. The filter is time-varying, however, since the time-shifted output is which is not the same as the filter applied to a time-shifted input ( ). Note that in applying the time-invariance test, we time-shift the input signal only, not the coefficients.

The filter , where is any constant, is nonlinear and time-invariant, in general. The condition for time invariance is satisfied (in a degenerate way) because a constant signal equals all shifts of itself. The constant filter is technically linear, however, for , since , even though the input signal has no effect on the output signal at all.

Any filter of the form is linear and time-invariant. This is a special case of a sliding linear combination (also called a running weighted sum, or moving average when ). All sliding linear combinations are linear, and they are time-invariant as well when the coefficients ( ) are constant with respect to time.

Sliding linear combinations may also include past output samples as well (feedback terms). A simple example is any filter of the form

 (5.7)

Since linear combinations of linear combinations are linear combinations, we can use induction to show linearity and time invariance of a constant sliding linear combination including feedback terms. In the case of this example, we have, for an input signal starting at time zero,

If the input signal is now replaced by , which is delayed by samples, then the output is for , followed by

or for all and . This establishes that each output sample from the filter of Eq.(4.7) can be expressed as a time-invariant linear combination of present and past samples.

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Nonlinear Filter Example: Dynamic Range Compression
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Time-Invariant Filters