I have two 2D Gaussian random variables. I'd like to find the weighted average of the two (based on their covariance matrix around the mean, meaning that the mean of the final Gaussian should be closer to the one with the smallest variance). What is the proper way of doing that? Any pointer to web urls around this topic would be highly appreciated.
Clarification:
I have a robot that has two sensors. The robot reads two bi-variate normal observations of an object. I want to find the location of the object by finding the weighted mean of the two samples. So I want my final location that I calculate to be closer to the observation that has a smaller spread. What is the proper way of doing that?
Best Answer
Presumably the sensors have been calibrated for no bias. Let the first sensor return the vector $(X_1,X_2)$ with covariance matrix $\Sigma$ and the second one return $(Y_1,Y_2)$ with covariance matrix $T$. The expectation of a weighted linear combination $\mu(X_1,X_2)+\nu(Y_1,Y_2)$ is still unbiased if and only if $\mu+\nu=1$, while its covariance equals $\mu^2\Sigma + \nu^2 T$. This tells us the expectation of the squared error (squared Euclidean distance) equals $\mu^2(\sigma_{11}+\sigma_{22}) + \nu^2(\tau_{11}+\tau_{22})$, which is minimized when $\mu$ is proportional to $1/(\sigma_{11}+\sigma_{22})$ and $\nu$ is proportional to $1/(\tau_{11}+\tau_{22})$; the constant of proportionality is easily found from the $\mu+\nu=1$ restriction. (For an explanation of this see Intuitive explanation of contribution to sum of two normally distributed random variables.)