What you have are not the poles and zeros, but simply the filter coefficients, i.e., the coefficients of the numerator and denominator polynomials.
The poles are the roots of the denominator polynomial, and the zeros are the roots of the numerator polynomial. In Matlab they can be found by using the roots command:
p = roots(a);
z = roots(b);
Note that in ...
I believe that h(-t) means a "time-reversed" version of h(t). Your command: 'y = conv(r,-h);' computes the convolution of 'r' and negative 'h', and you don't want that. I think you want:
y = conv(r,conj(fliplr(h)));
You are really close! Change your signal and steering vectors to be complex. Specifically for the steering vectors, these coefficients are meant to act as phase shifts. Using a real sinusoid will introduce a phase shift term in the opposite angle direction, which you don't want. Doing this alone you will see an improvement in your pseudospectrum.
In regards ...
We discussed this phenomenon on the music-dsp mailing list in May 2014.
For long enough a period, the audible repetition is not directly about the frequency spectrum but that instances of white noise are not white but usually contain distinct features or patterns that can be learned and then recognized in the later periods. In some instances, there will be ...
Rather than generating a longer block of data, i would like to make a longer file by concatenating the block of 4096 samples.
Bad idea. That means your noise becomes perfectly correlated with a period of 4096 samples, and that's definitely not white noise anymore, and you'll stand a realistic chance of noticing that audibly; depending on the sampling rate, ...
If you plot the cross correlation instead of taking the maximum, then I expect you'd see the problem.
The cause is that your signals aren't centered around zero. They have an offset. The cross correlation of two signals with an offset is a kind of triangle looking thing with the peak at zero.
As far as the cross correlation is concerned, your two signal ...
It may be useful to have a deeper understanding of you satellite's dynamics.
What I mean is that by observing your attitude quaternion error variation DSP ( or using the motion mathematical model), you may be able to choose a convenient filtering type/tuning to get rid of most possible white noise.
Marcus' answer is perfect!
For any wireless channel, y = Hx + n,
where: y = received signal
x = transmitted signal
n = noise in the channel
For an AWGN channel, output 'y' = input 'x' plus 'noise' (AWGN) i.e. y = x + n
Therefore, for AWGN channel, channel matrix or H-matrix is the identity matrix.
"integrator is unstable since it has a pole on the unit circle" -- not true. This Z-1 is done all the time in CIC resamplers. The pole is --on-- the unit circle exactly, and is therefore stable. Fun fact, if you use floating point math this falls apart and the integrator blows up bc the value is not exactly 1, its 1.000000000001 (or something larger than 1)
Take a look at this repository.
It uses oriented gabor filters to perform noise removal, thereby enhancing the image. It can also recover broken ridges up to a certain extent.