Track association using correction for bias and missing data
Samuel S. Blackman, Nam D. Banh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
One technique for multisensor tracking forms sensor level tracks using measurements received from the individual sensors. Then, the sensor level tracks are combined into a central level trackfile by performing multisensor track-to-track association and track fusion. Due to difference in sensor resolution, detection capability and coverage, there may be targets for which a track is formed by one sensor but not by the other. Also, tracks formed on the same target by multiple sensors may differ due to multisensor misalignment (or bias) error. This paper addresses these problems by developing a method to perform multisensor track-to-track association under the conditions of intersensor bias and missing track data. An augmentation to the association matrix is developed to account for the face that each sensor may not contain a full set of tracks for all targets in the field of view. An iterative approach is used to estimate and correct for the bias error. Monte Carlo simulation results are presented to illustrate the methods and close correspondence is found between these results and the theoretical probability of correct association.