PROJECT TITLE :
Typical tracking algorithms rely on the belief that the targets of interest are point supply objects. But, in realistic eventualities the purpose supply assumption is often not suitable and estimating the thing extent becomes an important aspect. Recently, a Bayesian approach to extended object tracking using random matrices has been proposed. At intervals this approach, ellipsoidal object extensions are modeled by random matrices and treated as further state variables to be estimated. But, solely one-object solution has been presented therefore way. In this work we gift the multi-object extent of this approach. We tend to derive a new variant of probabilistic multi-hypothesis tracking (PMHT) that simultaneously estimates the ellipsoidal form and therefore the kinematics of every object using expectation-maximization (EM). Each the ellipsoids and the kinematic states are iteratively optimized by specific Kalman filter formulae that arise directly from the PMHT framework. The novel methodology is demonstrated and evaluated by simulations.
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