This paper addresses multi-sensor surveillance where some sensors provide intermittent, feature-rich information.
Effective exploitation of this information in a multi-hypothesis tracking context requires computationally-intractable
processing with deep hypothesis trees. This report introduces two approaches to address this problem, and compares
these to single-stage, track-while-fuse processing. The first is a track-before-fuse approach that provides computational
efficiency at the cost of reduced track continuity; the second is a track-break-fuse approach that is computationally
efficient without sacrificing track continuity. Simulation and sea trial results are provided.
The track repulsion effect induces track swapping in difficult target-crossing scenarios. This paper provides a simple
analytical model for the probability of successful tracking in this setting. The model provides a means to quantify the
degree-of-difficulty in target-crossing scenarios. We analyze model-based performance predictions for a range of
scenario parameters. Additionally, we provide simulation results with a multi-hypothesis tracker that confirm the
increased performance challenge in crossing target settings as the ambiguity persists longer, i.e. as the targets cross
Multi-sensor fusion of data from maritime surveillance assets provides a consolidated surveillance picture that provides
a basis for downstream semi-automated anomaly-detection algorithms. The fusion approach that we pursue in this
paper leverages technology previously developed at NURC for undersea surveillance. We provide illustrations of the
potential of these techniques with data from recent at-sea experimentation.