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A phase-only filter for image correlation that is derived from the mathematical representation of an optical correlator has been shown to be applicable to the data association problem. A new approach for this type of correlation that can also incorporate measurement and track uncertainties is developed. Data association is a key component of the data fusion process; Level 1 data fusion in target tracking consists of kinematics (position and velocity) and classification. The phase-only filter correlation approach can be used in both the kinematics and the classification track-elements, but its the kinematic elements of the target track that are considered in this work. Inspired by the unscented Kalman filter, a sample-point distribution technique is developed that incorporates the uncertainty into the POF association technique.