Solution to time offset vector equation?

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Discussion Overview

The discussion revolves around solving a vector equation related to time offset between two data signals, u(t) and v(t), within the context of linear algebra. Participants explore the challenges of aligning these signals computationally without manual adjustments, particularly when the data is discrete and not continuous.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents a vector equation involving a time offset and seeks to understand how to solve for A, the matrix that relates the two signals.
  • Another participant questions the notation used, suggesting that A should be a matrix rather than a scalar, as the left side of the equation represents a vector.
  • The original poster acknowledges the confusion in notation and clarifies that the problem is actually a sampling issue, describing specific signal vectors and their relationship with the time offset.
  • A later reply suggests that the problem can be approached using cross-correlation, providing links to external resources for further exploration.

Areas of Agreement / Disagreement

Participants express differing views on the notation and the nature of the problem, with some agreeing on the need for clarification while others propose alternative methods like cross-correlation. The discussion remains unresolved regarding the best approach to solve the original problem.

Contextual Notes

Participants note limitations related to the discrete nature of the signals and the challenges posed by larger time offsets that exceed the length of the signals. There is also mention of the need to insert "fake" points into the data vectors to address these issues.

Xuser
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Hello math goers,

My education in linear algebra is limited to an Intro course I took a year ago. So I am posting this to see if such a solution exists in the first place, at least so I can start learning about it.

The problem is: solve A for equation
[tex]u(t) = A \cdot v(t+t_o)[/tex]

[tex]\left[ \begin{array}{c} u(t_1)\\ u(t_2)\\ \vdots\\ u(t_n) \end{array} \right]=\left[ \begin{array}{cccc} a_1 & a_2 & \cdots & a_n \end{array} \right] \cdot \left[ \begin{array}{c} v(t_1+t_o) \\ v(t_2+t_o) \\ \vdots \\ v(t_n+t_o) \end{array} \right][/tex]

Where [tex]t_o[/tex] is some known offset with respect to [tex]t[/tex] .

Essentially what these represent are two data signals [tex]v(t_v)[/tex] and [tex]u(t_u)[/tex], these two signals have some small time offset that I can calculate using a reference peak that both signals contain. However, getting A is not as simple as solving for A because the data is not continuous.

The "easy" way around this is to manually align every single value [tex]v(t_v)[/tex] with [tex]u(t_u)[/tex], but this is computationally expensive.

Any thoughts?
 
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Your notation looks a bit strange. The right hand side would be a scalar since it is the inner product of two vectors. On the other hand, the left side is a vector. Shouldn't A be a matrix?

Torquil
 
torquil said:
Your notation looks a bit strange. The right hand side would be a scalar since it is the inner product of two vectors. On the other hand, the left side is a vector. Shouldn't A be a matrix?

Torquil

Yes, sorry about that, it should look like:

[tex]\left[ \begin{array}{c} u(t_1)\\ u(t_2)\\ \vdots\\ u(t_n) \end{array} \right]=\left[ \begin{array}{c} a_1 \\ a_2 \\ \cdots \\ a_n \end{array} \right] \cdot \left[ \begin{array}{c} v(t_1+t_o) \\ v(t_2+t_o) \\ \vdots \\ v(t_n+t_o) \end{array} \right][/tex]

Where, [tex]a_n[/tex] is some scaling constant. For simplicity, I am just going to go ahead and let [tex]a_n=1;[/tex] for all n. So that the equation now is:

[tex]u(t) = v(t+t_o)[/tex]

Which I just realized isn't really an equation at all... my problem is a sampling problem. If I have some time vector
[tex]t=\left( \begin{array}{c}1\\ 2\\ 3\\ 4\\ 5\\ 6 \end{array}\right)[/tex]

and signal vectors, representing step function:
[tex]u=\left( \begin{array}{c} 0\\ 0\\ 0\\ 1\\ 1\\ 1\end{array}\right); \; v=\left( \begin{array}{c} 0\\ 2\\ 2\\ 2\\ 0\\ 0 \end{array}\right)[/tex]

Then [tex]t_o= -2[/tex], and [tex]a_n= \frac{u(t_n)}{v(t_n-2)}[/tex]
which happens to be equal to 2 or 0 in this case.

Because this is a discrete signal, whenever [tex]t_o[/tex] is larger than the length of the signal, this will not work. I think I need to insert "fake" points in the data vectors for this case.

Perhaps this thread belongs in the "Computing & Technology" forum?
 

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