  Ensuring sufficiently accurate models is crucial in target tracking systems.
If the assumed models deviate too much from the truth, the tracking performance
might be severely degraded. While the models are in general defined using
multivariate conditions, the measures used to validate them are most often
scalar-valued. In this paper, we propose matrix-valued measures for both
offline and online assessment of target tracking systems. Recent results from
Wishart statistics, and approximations thereof, are adapted and it is shown how
these can be incorporated to infer statistical properties for the eigenvalues
of the proposed measures. In addition, we relate these results to the
statistics of the baseline measures. Finally, the applicability of the proposed
measures are demonstrated using two important problems in target tracking: (i)
distributed track fusion design; and (ii) filter model mismatch detection.
