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This paper presents a concept for an end-to-end system to evaluate the effectiveness of Maritime Situational Awareness (MSA) assets. One such prototype system, the Maritime Surveillance Simulator (MSS), developed at the NATO Undersea Research Centre (NURC) is introduced. The MSS system is highly modular and includes a global traffic model for large vessel traffic, sensor coverage models, a maritime data fusion engine, and metrics for overall system effectiveness. This paper describes the prototype MSS system and suggests further applicability for such a concept.

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.