Title :
Tracking and identification for closely spaced objects in clutter
Author :
Salmond, D.J. ; Fisher, D. ; Gordon, N.J.
Author_Institution :
Defence Evaluation & Res. Agency, UK
Abstract :
The sampling based bootstrap filter is applied to a measurement association and classification problem for two adjacent objects which gradually separate. The problem is to apply position and discrimination (signature) information to identify and track the objects. Due to the object proximity and the presence of dense clutter, the association between measurement/classification data and the objects is initially highly uncertain. The bootstrap filter is employed to integrate the available information in near-optimal fashion without recourse to complex hypothesis formulation. Thus the posterior distribution of the two objects is generated.
Keywords :
identification; sampling methods; statistical distributions; adjacent objects; classification problem; closely spaced objects; clutter; complex hypothesis formulation; identification; measurement association; near-optimal fashion; object proximity; posterior distribution; sampling based bootstrap filter; Bayes methods; Clutter; Mathematical model; Measurement uncertainty; Noise; Q measurement; Time measurement; Estimation; aerospace; stochastic;
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6