Classification Aided Cardinalized Probability Hypothesis Density Filter

Target class measurements, if available from automatic target recognition systems, can be incorporated into multiple target tracking algorithms to improve measurement-to-track association accuracy. In this work, the performance of the classifier is modeled as a confusion matrix, whose entries are target class likelihood functions that are used to modify the update equations of the recently derived multiple models CPHD (MMCPHD) filter. The result is the new classification aided CPHD (CACPHD) filter. Simulations on multistatic sonar datasets with and without target class measurements show the advantage of including available target class information into the data association step of the CPHD filter.