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This PR adds an example that implements an algorithm [1] that allows to deal with Out-of-sequence measurements (OOSM) while running a particle filter.
The algorithm works by "injecting" the delayed measurement into the particle track history and re-evaluate the particle weights. We compare as well a tracker where we ignore the OOSM and we see that the difference can be significant.
Other papers referenced are [2] and [3].
[1] M. Orton and A. Marrs, 2001, A Bayesian approach to multi-target tracking and data fusion with out-of-sequence measurements, IEE Target Tracking: Algorithms and Applications. [2] M. Orton and A. Marrs, 2005, Particle filters for tracking with out-of-sequence measurements, IEEE Transactions on Aerospace and Electronic Systems. [3] S. R. Maskell, R. G. Everitt, R. Wright, M. Briers, 2005, Multi-target out-of-sequence data association: Tracking using graphical models, Information Fusion.