An exact solution to track-to-track-fusion at arbitrary communication rates

Track-to-track fusion (T2TF) aims at combining locally preprocessed information of individual trackers at a fusion center. Particularly, such schemes are obligatory in many applications of distributed sensors because of limited communication resources. If T2TF yields equivalent results compared with a Kalman filter (KF) processing all measurements from every sensor, it might be called optimal T2TF. It is well known that this can be achieved for deterministically moving targets, or if the local sensor tracks produced at all individual scan times are available in the fusion center. However, if such a full-rate communication is not available, achieving optimal performance is a great challenge due to cross-correlations between the local tracks. In this paper, we show that track decorrelation and therefore optimal T2TF can be achieved if sensor properties such as the measurement error covariance and measurement function are known at the each tracking system. To this end, local tracks have to be modified and local optimality has to be given up. As a result, we provide a distributed Kalman-type processing scheme for maneuvering targets, which yields optimal T2TF results at arbitrarily chosen instants of time by communicating and combining the local sensor tracks. Applications can be found in tracking scenarios with dynamically changing bandwidth constraints.