Fitting a vector to another

I have two vectors x1 and x2 (2 data sets) of two sensors measuring the same value (redundancy measurement). Correlation is almost 0.99). I want to adjust one vector (x1) to another (x2) (using other as a reference). I am currently doing 1x/x2 (simple normalization). Any other options? Thanks

[Image Analyst]

Give a small example. Let's say x1=[1,2,3] and x2 = [1.110, 1.999, and 3.042]. What do you want as output?

[Asl]

I have to use the reading of X1 for some reasons but X2 is more relaibale and accurate. Thus I am normalizinh x1 by dividing it is x2 to fit X1 according to the X2.

Answer 1

[John D'Errico]

You are the one, the only one, who can make a decision like this.

You have posed an implicit model:

x1 = k*x2 + e

where k is an unknown parameter to be found in your calibration scheme, and e is a vector of errors.

So the question is what model do YOU think makes sense? Then we can see how to estimate it. For example, might there be a constant offset on one axis or the other?