15 April 2010 Wide-area feature-aided tracking with intermittent multi-sensor data
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Abstract
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.
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Craig Carthel, Craig Carthel, Stefano Coraluppi, Stefano Coraluppi, Karna Bryan, Karna Bryan, Gianfranco Arcieri, Gianfranco Arcieri, } "Wide-area feature-aided tracking with intermittent multi-sensor data", Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 76981B (15 April 2010); doi: 10.1117/12.855711; https://doi.org/10.1117/12.855711
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