Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements of the target states provided by one or multiple sensors. Additional information, such as imperfect estimates of target classes provided by a classifier, can facilitate the target-measurement association and thus improve MTT performance. In this letter, we describe how a recently proposed MTT framework based on the sum-product algorithm can be extended to efficiently exploit class information. The effectiveness of the proposed approach is demonstrated by simulation results.
@article{arxiv.2008.01667,
title = {Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm},
author = {Domenico Gaglione and Giovanni Soldi and Paolo Braca and Giovanni De Magistris and Florian Meyer and Franz Hlawatsch},
journal= {arXiv preprint arXiv:2008.01667},
year = {2020}
}
Comments
Accepted to be published in IEEE Signal Processing Letters. The document includes a supplementary material