Ángel F. García-Fernández

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This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in(More)
This paper addresses the problem of simultaneously localizing multiple targets and estimating the positions of the sensors in a sensor network using particle filters. We develop a new technique called multitarget simultaneous localization and mapping (MSLAM) that has better performance than the well-known FastSLAM when there are several targets in the(More)
This paper addresses the problem of detecting and tracking multiple targets in a Bayesian framework. First, we introduce the definition of Joint MultitracK Probability Density (JMKPD) which is the probability of having a certain number of tracks, each one clearly identified with an ID number, and a kinematic state. We develop the a priori model needed to(More)
A theoretical analysis is presented of the correction step of the Kalman filter (KF) and its various approximations for the case of a nonlinear measurement equation with additive Gaussian noise. The KF is based on a Gaussian approximation to the joint density of the state and the measurement. The analysis metric is the Kullback-Leibler divergence of this(More)
This paper presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current multitarget state estimate that minimizes the mean-square(More)
—This paper presents the generalized optimal sub-pattern assignment (GOSPA) metric on the space of finite sets of targets. Compared to the well-established optimal sub-pattern assignment (OSPA) metric, GOSPA is unnormalized as a function of the cardinality and it penalizes cardinality errors differently, which enables us to express it as an optimisation(More)
This paper introduces two generalizations of the celebrated auxiliary particle filter for multiple target tracking. The inherent difficulty of this problem is caused by the sampling of a high dimension state space, giving rise to the curse of dimensionality, which pulls down the performance of direct generalizations of single target particle filter(More)
We decompose a probability density function (PDF) of a labelled random finite set (RFS) into a probability mass function over a set of labels and a PDF on a vector-valued multitarget state given the labels. Using this decomposition, we write the Bayesian filtering recursion for labelled RFSs in an explicit form. The resulting formulas are of conceptual and(More)
The objective of this paper is to approximate the unlabelled posterior random finite set (RFS) density in multitarget tracking (MTT) using particle filters (PFs). The unlabelled posterior can be equivalently represented by any labelled density that belongs to the posterior RFS family. For the limited number of particles used in practice, PFs that assume(More)