On the sequential track correlation algorithm in a multisensor data fusion system

In this paper, sequential track correlation algorithm in a multisensor data fusion system is presented. It is well known that the state estimates obtained from a Kalman filter have correlated errors in time. While the innovations are white, this does not carry over to the state estimation errors. It should also be pointed out that the use of a sliding window for track-to-track association with the (appropriate) caveat that the distribution of the sum of chi-square variables over the window is only approximately chi-square distributed.