Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm

TL;DR

This paper enhances multitarget tracking by integrating classifier-derived class information into a sum-product algorithm framework, improving target association accuracy demonstrated through simulations.

Contribution

It introduces an extension of the sum-product algorithm for MTT that effectively incorporates class information from classifiers, a novel approach in this context.

Findings

Improved target-measurement association accuracy.

Effective exploitation of class information demonstrated in simulations.

Enhanced multitarget tracking performance.

Abstract

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.