A Fast Track to Track Association Algorithm by Sequence Processing of Target States

Track association and fusion posses great importance in distributed sensor systems. In this study, we propose a novel statistical method based on temporal covariance estimation of local sensor tracks in a time interval and an association cost based on Mahalanobis distance is derived to associate tracks from different sensors. Contrary to associating the sensor tracks at each scan, we use a sequence of statistical features obtained from a state-space based tracking algorithm and process them in blocks to reduce false association probability. The effectiveness of the proposed method is illustrated by various three-dimensional multi-target tracking simulation scenarios. The algorithm is fast. As the number of tracks to associate increases, the computation time increases only linearly while the degradation in the association performance is small.