Signal averaging

Signal averaging ( engl. signal averaging) is a method by which the noise can be reduced in repetitive signals. By additive superimposition of the useful signal is thereby enhanced while the superposition of the random noise, resulting in no amplification. Thus, the signal -to-noise ratio is improved. Conditions to ensure that a signal averaging can be applied are:

• It is a repetitive signal can be synchronized in time
• Signal and noise are uncorrelated
• The noise is random, so that its arithmetic mean in the frequency range zero ( white noise )

Examples

Biosignals

Elektroenzophalogramme (EEG ) are often superimposed by various processes in the brain. Through specific stimuli (eg optical or acoustic ) and synchronized recording of EEG, so-called evoked potentials are derived. The number of necessary in practice for a good result evoked responses depends on the signal -to-noise ratio and is different depending on the modality and physical characteristics.

From a subject in a psychophysiological experiment several biosignals, including, for example, skin conductance or the pupil diameter can be measured in order to measure the state of arousal and the cognitive processing load relative to a given stimulus. Here, this stimulus is repeatedly presented to the subjects. Thus, there arise signal curves up on some caused by external influences artifacts and errors in the measurement system should be equal. If these influences are not systematically causes and thus apprehended as noise, then the signal averaging means can be usefully applied.

Traffic monitoring

In the traffic control signal averaging can be used to create a current background image when the camera remains in a fixed position. Rate this wallpaper automatically adjusts to changing light conditions, for example if regular sums the last 32 image signals and averaged, it can then be useful for operations for object recognition. However, the noise component is ( traffic ) this is not a random and obstruction can occur in an image sequence several times, for instance when it stops.

• Signal processing