How Far Are Two Symmetric Matrices From Commuting? With an Application to Object Characterisation and Identification in Metal Detection

ABSTRACT Examining the extent to which measurements of rotation matrices are close to each other is challenging due measurement noise. To overcome this, data is typically smoothed, and the Riemannian and Euclidean metrics are applied. However, if rotation matrices are not directly measured and are instead formed by eigenvectors of measured symmetric matrices, this can be problematic if the associated eigenvalues are close. In this work, we propose novel semi‐metrics that can be used to approximate the Riemannian metric for small angles. Our new results do not require eigenvector information and are beneficial for measured datasets. There are also issues when comparing rotational data arising from computational simulations, and it is important that the impact of the approximations on the computed outputs is properly assessed to ensure that the approximations made and the finite precision arithmetic are not unduly polluting the results. In this work, we examine data arising from object characterisation in metal detection using the complex symmetric rank two magnetic polarizability tensor (MPT) description; we rigorously analyze the effects of our numerical approximations and apply our new approximate measures of distance to the commutator of the real and imaginary parts of the MPT to this application. Our new approximate measures of distance provide additional feature information, which is invariant of the object orientation, to aid with object identification using machine learning classifiers. We present the Bayesian classification examples to demonstrate the success of our approach.