Message ID: 16001
Entry time: Tue Apr 6 18:46:36 2021
In reply to: 15991
Reply to this: 16005

Author:

Anchal, Paco

Type:

Update

Category:

SUS

Subject:

Updates on recent efforts

As mentioned in last post, we earlier made an error in making sure that all time series arrays go in with same sampling rate in CSD calculation. When we fixed that, our recursive method just blew out in all the efforts since then.

We suspect a major issue is how our measured sensing matrix (the cross-coupling matrix between different degrees of freedom on excitation) has significant imaginary parts in it. We discard the imaginary vaues and only use real parts for iterative method, but we think this is not the solution.

Here we present cross-spectral density of different channels representing the three sensed DOFs (normalized by ASD of no excitation data for each involved component) and the sensing matrix (TF estimate) calculated by normalizing the first cross spectral density plots column wise by the diagonal values. These are measured with existing ideal output matrix but with the new input matrix. This is to get an idea of how these elements look when we use them.

Note, that we used only 10 seconds of data in this run and used binwidth of 0.25Hz. When we used binwidth of 0.1 Hz, we found that the peaks were broad and highest at 13.1 Hz instead of 13 Hz which is the excitation frequency used in these measurements.

How should we proceed?

We feel that we should figure out a way to use the imaginary value of the sensing matrix, either directly or as weights representing noise in that particular data point.

Should we increase the excitation amplitude? We are currently using 500 counts of excitation on coil output.

Are there any other iterative methods for finding the inverse of the matrix that we should be aware of? Our current method is rudimentary and converges linearly.

Should we use the absolute value of the sensing matrix instead? In our experience, that is equivalent to simply taking ratios of the PSD of each channel and does not work as well as the TF estimate method.