A No-Loss Covariance Intersection Algorithm for Track-to-Track Fusion

Compared to the optimal track-to-track fusion (T2TF) algorithm under linear Gaussian assumption and the information matrix fusion, the major advantage of the covariance intersection (CI) method for the problem of T2TF is that it neither needs the crosscovariances between the local tracks, nor does it use local information increments which are required to be independent. This allows the CI method to be used in scenarios where the optimal T2TF and the information matrix fusion algorithms are difficult or impractical to implement. However, a significant drawback of the original CI algorithm is that it is too conservative and will yield unnecessary loss in its calculated fusion accuracy. Even worse, as shown in this paper, this loss increases with the number of local tracks for fusion. This greatly degrades the usefulness of the CI algorithm. In this paper, a new "sampling CI" algorithm is proposed, which is simple to implement and does not have the above problematic feature of the original CI. Simulation results from various scenarios demonstrate the effectiveness of the proposed algorithm.