I\'m using the Matlab function Y = WGN(M,N,P)
to generate white noise with Gaussian distribution. This function uses a power value (dB Watts) to calculate the
why you just take randn function of whatever bound and then just normalize it like this ex.
noise=randn(400); noise=noise./max(max(noise));
so whatever is the output of randn finally you will have a w.n. inside [-1 1].
As others have said, you can't limit a Gaussian distribution. What you can do is define your range to be 6 standard deviations, and then use randn(m,sigma) to generate your signal.
For example if you want a range of [-1 1] you will choose sigma=2/6=0.333 and Mu=0. This will create a chance of 99.7% to be inside the range. You can then round up and down those numbers that are out of the range.
This will not be a pure Gaussian distribution, but this is the closest you can get.
You can use rand
rather than Gaussian generator. The output range of rand
is 0-1, so to make it in the range -1 1 you use rand(args)*2 -1
.
It should be noted that this generator is sampling a uniform density.
Don't want to say something very wrong, but when I copied your code and changed
RandomSignal = .25*wgn(10000,1,1,1,'linear');
it was then ok. Hope it works for you.(Assuming random data/4 is still random data)
Gaussian noise has an unbounded range. (The support of the Gaussian pdf is infinite.)