Statistical Multitarget Filtering and Information Fusion of Detection and Class Measurements

Advanced machine vision and deep learning models are increasingly used as virtual sensors to detect and classify targets via class probabilities. Common statistical multitarget filters and their associated information fusion methods rely on point measurements, and integrating target class measurements into those solutions is challenging. This paper introduces a statistical multitarget filter, formulated in the labeled multi-Bernoulli filtering framework to handle class measurements from virtual sensors. Applied to multi-sensor, multitarget filtering in centralized and distributed networks, numerical experiments show that incorporating class information enhances tracking accuracy, especially when shared across networks. The results highlight the benefits of using class probabilities to improve tracking in dynamic environments.