I have two estimates of two points in space – each has a 3D position and a cigar shaped 3×3 covariance matrix and I am checking the hypothesis that these observations are actually referencing the one and same point. So I would like to calculate the agreement of the two observations with this assumption.
A search brings up Bhattacharyya distance, or Kullback–Leibler divergence as candidates. I am not looking for the most correct estimate, but rather an easy to implement function which takes two positions and two 3×3 matrices and returns a percentage or distance in standard deviations.
Here are some similar threads:
Mahalanobis distance between two bivariate distributions with different covariances
Measures of similarity or distance between two covariance matrices
Best Answer
In the end I went for the Bhattacharyya distance. I adapted the R code referenced here: