Work

Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm

Year: 2020

Type: article

Abstract: Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements 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.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2020.3024858

Authors: Domenico Gaglione, Giovanni Soldi, Paolo Braca, Giovanni De Magistris, Florian Meyer, Franz Hlawatsch