Switched-Markov filtering for tracking maneuvering targets

A new filtering concept is presented for tracking maneuvering targets. A conventional Markov switching process is used to model the target maneuver process, but a new filtering scheme is used. The filter uses a traditional track-splitting approach, with one Kalman filter tuned to each branch of the tree. To limit filter complexity, aggregation is performed over the earliest timestep of an arbitrary filter memory length. Before aggregation, a unique consistency update stage is used where each of the filter's state estimates is compared with the associated conditional model for that filter. If the two are inconsistent (e.g., a large acceleration component generated from a nonmaneuvering model), less weight is placed on that estimate. Results are presented from a full 3-D tracking model.