3

My first swing at the answer had some very incorrect claims. I do not have access to the article, so I am inferring some things from the portion posted in the question. NOTA BENE: My arguments assume that the eigenvectors of $\mathbf{R}$ are arranged so that the first $n$ belong to the signal subspace and that the last $m-n$ belong to the noise subspace. ...


3

Yes. The FIR filter model you're used to is a series of Neurons with weighted inputs, and a linear activation function. In other words, a standard FIR filter is a neural network. I mean, it's called "CNN". The C is exactly the operation a filter does.


3

Your 2-mic array can provide an angle-of-arrival estimate with front-back ambiguity, using cross-correlation to estimate TDOA and then calculating AOA from TDOA and microphone spacing. The front-back ambiguity can be removed with sufficiently directional microphones, or just by constraining the geometry, putting the microphones against walls, for example. ...


2

Depending on the window function, you may be able to use a DFT-even version of the window function. "DFT-even" means that the periodic extension of the window function is symmetrical. In MATLAB and Octave you can get such a window like this (the first line in the source code): a = hanning(10, "periodic"); b = fftshift(a); c = a + b; plot(...


1

As far as I can tell from the graph, the variance of the signal goes up substantially under "oscillation" conditions. So, monitor the variance over a rolling window. High variance indicates oscillation. To choose the window width, consider: if the window is too short, the computed variance will be too noisy if the window is too long, the monitor will be ...


1

I'm going to group number 1 and 3 as related. For a high level description of MUSIC, you can take a look at MATLAB's overview here. One of the main steps in the algorithm is to find the eigenvectors of a correlation matrix, which can be done via singular value decomposition or other methods. MATLAB has functions for this, so you may want to find equivalent ...


1

You must be assuming you have a dominant peak at zero from only looking at the same graph you shared, but if you really did remove the mean, then value at bin 0 will be 0 (as bin 0 is directly proportional to the mean). Inspect the data carefully as their does not appear to be anything wrong with the code with respect to the FFT. What is likely occurring is ...


1

Are you using scipy by any chance ? If yes , this might help https://scikit-dsp-comm.readthedocs.io/en/latest/_modules/sk_dsp_comm/digitalcom.html check for functions "QPSK_tx" and "QPSK_bb"


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