Multiellipsoidal extended target tracking with known extent using sequential Monte Carlo framework

Abstract

In this paper, we consider a variant of the extended target tracking (ETT) problem, namely the multiellipsoidal ETT problem. In multiellipsoidal ETT, target extent is represented by multiple ellipses, which correspond tothe origin of the measurements on the target surface. The problem involves estimating the target’s kinematic state andsolving the association problem between the measurements and the ellipses. We cast the problem in a sequential MonteCarlo (SMC) framework and investigate different marginalization strategies to find an efficient particle filter. Under theknown extent assumption, we define association variables to find the correct association between the measurements andthe ellipses; hence, the posterior involves both discrete and continuous random variables. By expressing the measurementlikelihood as a mixture of Gaussians we derive and employ a marginalized particle filter for the independent associationvariables without sampling the discrete states. We compare the performance of the method with its alternatives andillustrate the gain in nonstandard marginalization.