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Statistical signal processing is an area of Applied Mathematics and Signal Processing that treats signals as stochastic processes, dealing with their statistical properties (e.g., mean, covariance, etc.). Because of its very broad range of application Statistical signal processing is taught at the graduate level in either Electrical Engineering, Applied Mathematics, Pure Mathematics/Statistics, or even Biomedical Engineering and Physics departments around the world, although important applications exist in almost all scientific fields. == Basic Signal Model == In many applications, a signal is modeled as functions consisting of both a deterministic and a stochastic component. A simple example and also a common model of many statistical systems is a signal that consists of a deterministic part added to noise which can be modeled in many situations as white Gaussian noise : : where : White noise simply means that the noise process is completely uncorrelated. As a result, its autocorrelation function is an impulse: : where : 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「statistical signal processing」の詳細全文を読む スポンサード リンク
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