An approximate minimum MOSPA estimator
Paper i proceeding, 2011
Optimizing over a variant of the Mean Optimal Subpattern
Assignment (MOSPA) metric is equivalent to optimizing over
the track accuracy statistic often used in target tracking benchmarks.
Past work has shown how obtaining a Minimum MOSPA
(MMOSPA) estimate for target locations from a Probability
Density Function (PDF) outperforms more traditional
methods (e.g. maximum likelihood (ML) or Minimum Mean
Squared Error (MMSE) estimates) with regard to track accuracy
metrics. In this paper, we derive an approximation to the
MMOSPA estimator in the two-target case, which is generally
very complicated, based on minimizing a Bhattacharyya-like
bound. It has a particularly nice form for Gaussian mixtures.
We thence compare the new estimator to that obtained from
using the MMSE and the optimal MMOSPA estimators.