Wide-area feature-aided tracking with intermittent multi-sensor data

Craig Carthel; Stefano Coraluppi; Karna Bryan; Gianfranco Arcieri

doi:10.1117/12.855711

15 April 2010 Wide-area feature-aided tracking with intermittent multi-sensor data

Craig Carthel, Stefano Coraluppi, Karna Bryan, Gianfranco Arcieri

Proceedings Volume 7698, Signal and Data Processing of Small Targets 2010; 76981B (2010) https://doi.org/10.1117/12.855711
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Craig Carthel, Stefano Coraluppi, Karna Bryan, and 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); https://doi.org/10.1117/12.855711

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