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16 September 2011 Spectrally assisted target tracking
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There has been interest in overhead tracking of automobiles on our roadways using optical sensors. Tracking of multiple vehicles can be accomplished with a single band high-resolution sensor as long as the vehicles are continuously in view. However, in many cases the vehicles pass through or behind blackouts, such as through tunnels or behind tall buildings. In these cases, the vehicles of interest must be reacquired and recognized from the collection of vehicles present after the blackout. The approach considered here is to add an additional sensor to assist a single band high-resolution tracking sensor, where the adjunct sensor measures the vehicle signatures for recognition and reacquisition. The subject of this paper is the recognition of targets of interest amongst the observed objects and the reacquisition after a blackout. A Generalized Likelihood Ratio Test (GLRT) algorithm is compared with the Spectral Angle Mapper (SAM) and Euclidian distance algorithms. All three algorithms were evaluated on a database of signatures created by measuring samples from old automobile gas doors. The GLRT was the most successful in recognizing the target after a blackout and could achieve a 95% correct reacquisition rate. The results show the feasibility of using a hyper spectral sensor to assist a multi target tracking sensor by providing target recognition for reacquisition.
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Lawrence E. Hoff and Edwin M. Winter "Spectrally assisted target tracking", Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 81370Z (16 September 2011);

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