Statistical signal processing

Statistical signal processing is an area of signal processing that treats signals as stochastic processes, dealing with their statistical properties (e.g., mean, covariance, etc.). Traditionally it is taught at the graduate level in electrical engineering departments around the world, although important applications exist in almost all scientific fields.

In many areas signals are modeled as functions consisting of both deterministic and stochastic components. A simple example and also a common model of many statistical systems is a signal $y(t)$ that consists of a deterministic part $x(t)$ added to noise which can be modeled in many situations as white Gaussian noise $w(t)$:

$y(t)=x(t)+w(t)\,$

where

$w(t)\sim {\mathcal {N}}(0,\sigma ^{2})$

White noise simply means that the noise process is completely uncorrelated. As a result, its autocorrelation function is an impulse:

$R_{ww}(\tau )=\sigma ^{2}\delta (\tau )\,$

where

$\delta (\tau )\,$ is the Dirac delta function.

Given information about a statistical system and the random variable from which it is derived, we can increase our knowledge of the output signal; conversely, given the statistical properties of the output signal, we can infer the properties of the underlying random variable.

These statistical techniques are developed in the fields of estimation theory, detection theory, and numerous related fields that rely on statistical information to maximize their efficiency.

See also

• Bayes theorem
• Bayes estimator

• Wiener filter
• Kalman filter

• Thomas Bayes
• Norbert Wiener
• Rudolph Kalman