# Tag Info

1 vote

### Related to Additive white Gaussian noise (AWGN)

Additive White Gaussian Noise (AWGN) is typically assumed to be zero mean as this adds random fluctuations around the original signal without biasing it in some way. If there is a non-zero mean in ...

### Removing multiplicative noise (signal) in FFT

I think the key to understanding this problem is the Fourier relationship between multiplication and convolution. If you have multiplicative noise in the time domain, this equates to the convolution ...

### Related to Additive white Gaussian noise (AWGN)

The zero-mean assumption rests on empirical validation. You can do it yourself: turn on an oscilloscope and set it to the minimum amplitude. You'll see noise with roughly zero mean. Intuitively, this ...
1 vote

### Modelling MEMS accelerometer noise

If your main focus is the end result (simulating the behaviour of an IMU, rather than learning to model the INS response from first principles) I'd recommend you cosnider gnss-ins-sim which will ...
Accepted

### Modelling MEMS accelerometer noise

Yes, you can use the datasheet noise density. Model the accelerometer as a source of white noise, and compute its characteristics. That works to the extent that you can trust the data sheet. That's ...

### Frequency content of a noisy signal

Couple of mistakes! #1 Take the fourier transform of its power signal (square the noisy signal) That's for anything but a constant zero signal not the same as the PSD estimate you get from your #2. ...
1 vote

### How to detect faint, dense grains on an image?

You want to segment the image into "grain" and "non-grain" areas. Then you want to label the "grain" areas. Segmentation is easy in principle: you process the image into ...
1 vote

### Proper audio noise and artifact detection approach

That's a tough problem to solve. There are commercial solutions for this type of measurements (e.g. Audio Precision (ap.com), Listen, Inc. (https://www.listeninc.com/). They do an ok job but tend to ...
Can white noise be correlated to a random singal Something can be correlated to something else but still be white. For example, let $X(t)$ be a white noise process, and let $Y(t) = 2X(t)$, then ...
As the DFT of real $X$ is conjugate symmetric, $\hat{X}$ is not N-dimensional jointly Gaussian and neither your two distributions is correct. Representing the N-dimensional DFT by a $2N$ dimensional ...