Optimal flow models for multiscan data association

Multiscan data association can significantly enhance tracking performance in critical radar surveillance scenarios involving multiple targets, low detection probability, high false alarm probability, evasive target maneuvers, and finite radar resolution. Unfortunately, however, this approach is affected by the curse of dimensionality which hinders its real-time application for tracking problems with short scan periods and/or a high number of scans of the association logics and/or many measurements per scan. It is shown here how the formulation of the multiscan association as a multi-commodity or single-commodity flow optimization problem allows a relaxation of the association problem which, on one hand, provides close-to-optimal association performance and, on the other hand, implies a significant reduction of the computational load.