Closely-spaced (but resolved) targets pose a challenge for measurement-to-track data association algorithms. Since the Mahalanobis distances between measurements collected on closely-spaced targets and tracks are similar, several elements of the corresponding kinematic measurement-to-track cost matrix are also similar. Lacking any other information upon which to base assignments, it is not surprising that data association algorithms make mistakes. This paper compares five methods for incorporating amplitude information to improve data association for multi-target tracking with Rayleigh targets.Two simple scenarios are used to demonstrate the impact of each method on measurement-to-track data association. None of the five methods perform best across the board. The analysis suggests that selection of a method for incorporating target amplitude information should be application-dependent.
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