Track-to-track association using intrinsic statistical properties

The problem of track-to-track association of local tracks from two disparate and dispersed sensor systems is considered. Typical approaches to this problem base the association upon the estimated target states. Bias, pointing, navigation and location errors among others often frustrate theses approaches and result in higher association error probabilities. Realizing that the state estimate represents on the first order statistics of the target trajectory, this paper augments those approaches with an association test based upon the second order statistics of the measurements. It is shown that in general, the cross-covariance of the measurements from two disparate and dispersed sensor systems will be nonzero if the measurements are from tracks on the same target. If the tracks are on different targets then the measurement crosscovariance will be zero. A test of the null hypothesis that the measurement cross-covariance is zero is derived and an implementation using sample statistics is developed. The probability density function of the test statistic is presented so that the test result can be combined with the association result based upon the estimated track state. Both absolute and relative tests are discussed. The effect of track length is analyzed and then examined in an example.