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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 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 devise a filtering algorithm to approximate the first two moments of the posterior probability density function (PDF). The novelties of the algorithm are in the update step. If the likelihood has a bounded support, we can use a modified prior distribution that meets Bayes' rule exactly. Applying a Kalman filter (KF) to the modified prior distribution,(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 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)
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 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 not normalised by the cardinality of the largest set and it penalizes cardinality errors differently, which enables us to express it as an(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)