Trackability: Resolvability of Two Closely-Spaced Targets

摘要

Recent research has developed a novel framework for determining target trackability using the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT). This framework allows for the calculation of the PDF of the peak point in the ML-PMHT log-likelihood ratio (LLR) due to clutter as well as the PDF of the peak point in the LLR due to the target. If it is possible to reliably discriminate between the peak target PDF and the peak clutter PDF, then the target is able to be tracked. We expand on this framework by adding a second target and determining the conditions under which both targets can be individually tracked. This work develops the first step toward that goal - it introduces the second target to the framework (an interfering target), and determines how close it can get to the original target before the peak generated by the original target is no longer distinguishable from the peak generated by the interfering target. At this point, the original target will no longer be trackable.