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A novel approach for extended object tracking is presented. In contrast to existing approaches, no statistical assumptions about the location of the measurement sources on the extended target object are made. As a consequence, a combined set-theoretic and stochastic estimator is obtained that is robust to systematic errors in the target model. The benefits of the new approach is demonstrated by means...
This paper presents a novel method for tracking multiple extended objects. The shape of a single extended object is modeled with a recently developed approach called Random Hypersurface Model (RHM) that assumes a varying number of measurement sources to lie on scaled versions of the shape boundaries. This approach is extended by introducing a so-called Mixture Random Hypersurface Model (Mixture RHM),...
This paper is about the use of symmetric state transformations for multi-target tracking. First, a novel method for obtaining point estimates for multi-target states is proposed. The basic idea is to apply a symmetric state transformation to the original state in order to compute a minimum mean-square-error (MMSE) estimate in a transformed state. By this means, the known shortcomings of MMSE estimates...
This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM) that assumes varying measurement sources to lie on scaled versions of the...
In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is derived. Each given measurement stems from an unknown point and is corrupted with additive Gaussian noise. Extreme value theory is applied in order to derive a linear measurement equation for the problem. The new estimator is applied to the problem of group target...
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