Hello! In Wikipedia they write: http://en.wikipedia.org/wiki/Additive_white_Gaussian_noise it is broadband noise or otherwise "white" noise, with constant spectral density (expressed in Watts per Hertz within the band under consideration) i Gaussian amplitude distribution .
Gaussian noise can come from many natural sources: thermal vibrations of the atomic lattice of the antenna material (called thermal or Johnson-Nyquist noise), the radiance of a black body, and also the Sun.
Therefore, there may also be "pink" noise (with a "privileged" frequency band) and a Gaussian distribution of amplitudes, as well as "white" noise with a non-Gaussian distribution (e.g. due to the operation of an amplitude limiter or, for example, a logarithm amplifier).
On this topic, Google gives you over 1.1 million pages in English...
The first thing I did was to use Google/Wikipedia and the books I have:
and I`m confused (and I`ve been sitting on it for about 2 hours now and reading it either I`m explaining it wrong or I didn`t understand something [I didn`t have a course like Signal Analysis - or similar and I have to learn it myself, so please be understanding])
It is often incorrectly assumed that Gaussian noise (ie noise with a Gaussian amplitude distribution — see normal distribution) is necessarily white noise. However, neither property implies the other. Gaussianity refers to the way signal values are distributed, while the term `white` refers to the shape of the power spectral density. White noise has a flat spectrum, similar to that of white ligh, hence the name.
so should I understand that White Gaussian Noise is White Noise in the appropriate distribution of occurrences (hence the word gaussian)? And that White noise is not the same as Gaussian noise?
because I don`t know what this ambiguity is (what makes them different)
- the term "white" refers only to spectral distribution , does not say anything about the amplitudes, so white noise remains white after passing through, for example, a logarithmic amplifier, but after passing through a selective amplifier there is no white noise anymore, it is "pink" or any other "colored" noise;
- the term "Gaussian" refers to amplitude distribution noise components, i.e. that some instantaneous amplitude is the most probable, and smaller and larger amplitudes have a probability consistent with the Gaussian distribution, but nothing is said about the spectral distribution, which means that Gaussian noise after passing through a linear selective amplifier remains Gaussian , while passing through a logarithm amplifier or amplitude limiter causes the noise to have a non-Gaussian distribution.
Oberon6: Gaussian noise is one in which the samples are independent random variables with a uniform Gaussian distribution. However, Gaussian white noise is Gaussian noise in which the average value of this common Gaussian distribution is also 0. Regards, Maciej
I have a question that will finally resolve this question.
To sum up, white noise is one whose signal spectrum is flat, i.e. the amplitudes of the signals constituting the spectrum are the same and theoretically range from 0 to infinity (in fact, some range). Now, if we take into account Gaussian white noise, the spectrum of such a WGN looks like a normal distribution with frequencies from a previously defined frequency range. That`s right ?
Gaussian noise and white Gaussian noise are related but distinct concepts. Gaussian noise refers to noise with an amplitude distribution that follows a Gaussian (normal) distribution, while white Gaussian noise (WGN) is a specific type of Gaussian noise characterized by a flat spectral density across frequencies, meaning it has equal power across the frequency spectrum. The term "white" indicates that the noise has a constant spectral density, whereas "Gaussian" pertains to the statistical distribution of the amplitudes. Thus, while all white Gaussian noise is Gaussian noise, not all Gaussian noise is white. The discussion also touches on the implications of passing these noises through various amplifiers, which can alter their characteristics. Summary generated by the language model.