CPHD Filter Birth Modeling Using the Probabilistic Admissible Region

Abstract

Sparse observations make single-point track initialization difficult in some multitarget tracking scenarios, including space-object tracking. A probabilistic admissible region approach combines physics- and scenario-based constraints in unobservable directions to reduce ambiguity in the initial state and allows for a birth model consistent with the derivation assumptions in the cardinalized probability hypothesis density filter. The proposed filter enables tracking of simulated space objects via optical or radar observations and establishes custody of newly observed targets when given sparse observations.