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This paper introduces an enhanced method for progressive Bayesian estimation based on a set of deterministic samples. The information of a given measurement is gradually introduced in order to avoid particle degeneration, which is usually encountered in standard particle filters. The main contribution of this paper is to derive a new method for exploiting smoothness assumptions about the unknown underlying...
We consider the problem of clustering set-valued observations, i.e., each observation is a set that consists of a finite number of real vectors. For this purpose, we develop a k-means algorithm that employs the OSPA distance for measuring the distance between sets. In particular, we introduce a novel alternating optimization algorithm for the OSPA barycenter of sets with varying cardinalities that...
We consider association-free tracking of multiple targets without identities. The uncertain multi-target state and the uncertain measurements cannot be described by a random vector as this would imply a certain order. Instead, they are described by an unordered random finite set (RFS). Particle-based random finite set densities are used for characterizing the RFS in a simple and natural way. For recursive...
In this paper, we address the problem of probability mass computation of a multivariate Gaussian contained within a polytope. This computation requires an evaluation of a multivariate definite integral of the Gaussian, whose solution is not tractable for higher dimensions in a reasonable amount of time. Thus, research concentrates on the derivation of approximate but sufficiently fast computation...
In state estimation theory, two directions are mainly followed in order to model disturbances and errors. Either uncertainties are modeled as stochastic quantities or they are characterized by their membership to a set. Both approaches have distinct advantages and disadvantages making each one inherently better suited to model different sources of estimation uncertainty. This paper is dedicated to...
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