Anoise

Anoise is a term used in signal processing and data analysis to denote a generalized additive perturbation that corrupts a measured or simulated signal. The concept serves as a placeholder for random fluctuations that accompany the true signal, and its exact statistical description can vary by field and model.

In a typical formulation, a observed signal y(t) equals the true signal s(t) plus an Anoise component N(t): y(t) = s(t) + N(t). The effect of Anoise on inference depends on its distribution, correlation structure, and power spectrum.

Common models range from Gaussian white Anoise, where N(t) has zero mean and flat spectrum, to colored Anoise with spectral density S(f) ∝ f^(-β). The parameter β governs color: β=0 white, β=1 pink, β=2 brown. The amplitude distribution may be Gaussian, Laplace, or heavy-tailed.

Generation and measurement: Anoise can be synthesized in simulations by passing white noise through linear filters or using random processes. In physical systems, sources include thermal noise, shot noise, quantization noise, and external interference; analysis often treats these as Anoise with assumed properties.

Applications and considerations: Anoise modeling is central to sensor fusion, communications, control systems, time-series analysis, and neuroscience. Choosing the correct Anoise model affects estimator bias, variance, and detection thresholds. The term is not universally standardized, and some disciplines prefer additive noise, Gaussian noise, or colored noise to describe similar phenomena.

See also: additive noise, white noise, colored noise, Gaussian process, stochastic process.