Journal ArticleDOI
A hybrid bootstrap filter for target tracking in clutter
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TLDR
The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered and a new hybrid bootstrap filter is proposed, an approach where random samples are used to represent the target posterior distributions.Abstract:
The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered. A new hybrid bootstrap filter is proposed. The bootstrap filter is an approach where random samples are used to represent the target posterior distributions. By using this approach, we circumvent the usual problem of an exponentially increasing number of association hypotheses as well as allowing the use of any nonlinear/non-Gaussian system and/or measurement models.read more
Citations
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On sequential Monte Carlo sampling methods for Bayesian filtering
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
Journal ArticleDOI
Particle filters for positioning, navigation, and tracking
Fredrik Gustafsson,Fredrik Gunnarsson,Niclas Bergman,Urban Forssell,Jonas Jansson,Rickard Karlsson,Per-Johan Nordlund +6 more
TL;DR: The technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map and it is shown that the accuracy is comparable with satellite navigation but with higher integrity.
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Particle filters for state estimation of jump Markov linear systems
TL;DR: This paper presents efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem forJump Markov linear systems.
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Data fusion for visual tracking with particles
TL;DR: Generic importance sampling mechanisms for data fusion are introduced and it is shown how each of the three cues can be modeled by an appropriate data likelihood function, and how the intermittent cues are best handled by generating proposal distributions from their likelihood functions.
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People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters
TL;DR: This paper introduces sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects using Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot.
References
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Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Journal ArticleDOI
Recursive bayesian estimation using gaussian sums
H. W. Sorenson,D. L. Alspach +1 more
TL;DR: A density approximation involving convex combinations of gaussian density functions is introduced and proposed as a meaningful way of circumventing the difficulties encountered in evaluating these relations and in using the resulting densities to determine specific estimation policies.
Proceedings ArticleDOI
Mixture reduction algorithms for target tracking in clutter
TL;DR: In this article, two new algorithms for reducing Gaussian mixture distributions are presented, which preserve the mean and covariance of the mixture, and the fmal approximation is itself a Gaussian mixture.
Journal ArticleDOI
Bayesian State Estimation for Tracking and Guidance Using the Bootstrap Filter
TL;DR: A Monte Carlo simulation example of a bearings-only tracking problem is presented, and the performance of the bootstrap filter is compared with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid filter.
Journal ArticleDOI
Stochastic simulation Bayesian approach to multitarget tracking
TL;DR: In this article, a stochastic simulation Bayesian method for multitarget tracking is developed, which uses a random sample in state space to represent the posterior state estimate distribution and is illustrated by simulations involving one target in dense clutter.