Tracking algorithms typically rely on point-to-point correlation of sensor positional data. When target positional or identification data is obscured, missing, or noisy, tracking algorithm performance degrades and may lose track before a subsequent measurement is available. There are many cases when partial target detection results such as when a target travels behind an obstacle or is occluded by trees or industrial camouflage. In an effort to design tracking algorithms that can track through missing, occluded or data dropout, we seek to use a group tracker technique to solve the occluded target tracking problem. Two methodologies are employed to compensate for a partially observable target state and covariance: (1) a coasting individual targets (CIT) method and (2) a group-updated track (GUT) method. The coasting method is analogous to the tracking prediction equations and the novel group tracking update method recovers the unobservable target position state from the other members of the group.