Exploiting correlation effects within multiple-hypothesis tracking

The need to track closely spaced targets in clutter is essential in support of military operations. This paper presents a Multiple-Hypothesis Tracking (MHT) algorithm which uses an efficient structure to represent the dependency which naturally arises between targets due to the joint observation process, and an Integral Square Error (ISE) mixture reduction algorithm for hypothesis control. The resulting algorithm, denoted as MHT with ISE Reduction (MISER), is tested against performance metrics including track life, coalescence and track swap. The results demonstrate track life performance similar to that of ISE-based methods in the single-target case, and a significant improvement in track swap metric due to the preservation of correlation between targets. The result that correlation reduces the track life performance for formation targets requires further investigation, although it appears to demonstrate that the inherent coupling of dynamics noises for such problems eliminates much of the benefit of representing correlation only due to the joint observation process.