Development of track to track fusion algorithms

This paper describes techniques for track level fusion of surveillance data that are applicable to existing and near term tactical surveillance systems. The linear optimal fused estimate is a convex combination of remote estimates with weights being the estimation confidences (covariances). The covariance based algorithm is most applicable where the track estimate is generated by a Kalman filter based tracking system. When track covariance is not available, such as in α-β tracking systems, an estimated covariance can be used for track fusion. In addition, track fusion also requires accounting for the cross covariance between tracks. Various approaches to estimating the auto covariances and the cross covariances are examined, and the performance is evaluated through computer simulations.