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Metrics for Feature-Aided Track Association

Track fusion over a network of sensors requires association of the tracks before the state estimates can be combined. Track association generally involves two steps: evaluating an association metric to score each track-to-track association hypothesis, and selecting the best assignment between two sets of tracks. In many applications feature-aided track association can provide better performance than association with only kinematic data (e.g., position and velocity) when the target density is high. This paper develops a general association metric to support feature-aided track association that considers similarity in both the feature and kinematic domains. The association metric is based upon the maximum a posteriori probability (MAP) approach and can be used for general target and sensor models. Special forms of the association metric are given for some common situations. Numerical results illustrate the performance of different feature association metrics