Using Target RCS when Tracking Multiple Rayleigh Targets

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 assignment cost matrix are also similar. Lacking any other information upon which to base assignments, it is not surprising that data association algorithms make mistakes. Given this problem many in the target tracking community have speculated that measurement-to-track data association could be improved through the use of features. Since the radar cross section (RCS) naturally occurs with the detection and kinematic measurement and does not require use of high-energy/high-resolution waveforms, it is typically a favored feature for radar systems. To gain insight into its impact on measurement-to-track data association, the benefits of RCS-assisted tracking are analytically assessed in several simple cases. Both the rigorous hypothesis probability approach and several less rigorous ad hoc approaches (which are commonly used in practice) are treated. Results vary between the approaches, but the conclusion is that the benefit of RCS-assisted tracking is limited. In fact applying it blindly in every dwell can actually degrade measurement-to-track data association. For this reason the steps required to incorporate RCS-assisted tracking in a beneficial manner are also identified.