Open AccessProceedings Article
Shape tracking of extended objects and group targets with star-convex RHMs
Marcus Baum,Uwe D. Hanebeck +1 more
- pp 1-8
TLDR
In this paper, a star-convex RHM is introduced for tracking star- Convex shape approximations of targets and Bayesian inference is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update.Abstract:
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 shape boundaries. In this paper, a star-convex RHM is introduced for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHMs is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.read more
Citations
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Journal ArticleDOI
A phd Filter for Tracking Multiple Extended Targets Using Random Matrices
Karl Granstrom,Umut Orguner +1 more
TL;DR: This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices, resulting in the Gaussian inverse Wishart phd (giw-phd) filter.
Journal ArticleDOI
Extended Target Tracking using a Gaussian-Mixture PHD Filter
TL;DR: Comment on the errors in the formulation of Theorem 1 and give a correct formulation of theorem.
Journal ArticleDOI
Corrections on: “Extended Target Tracking Using a Gaussian-Mixture PHD Filter”
TL;DR: This paper presents a Gaussian-mixture implementation of the probability hypothesis density (PHD) filter for tracking extended targets and suitable remedies are given to handle spatially close targets and target occlusion.
Journal ArticleDOI
Multiple Extended Target Tracking With Labeled Random Finite Sets
TL;DR: A new algorithm is proposed for tracking multiple extended targets in clutter, capable of estimating the number of targets, as well the trajectories of their states, comprising the kinematics, measurement rates, and extents, and results show that the (G)LMB has improved estimation and tracking performance.
Journal ArticleDOI
Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
TL;DR: This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion, including Markov Chain Monte Carlo methods, the random matrices approach and Random Finite Set Statistics methods.
References
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Book
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
TL;DR: With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory.
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TL;DR: This is a list of errors in the book Optimal State Estimation, John Wiley & Sons, 2006.
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TL;DR: This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time, and basic concepts are introduced with only essential mathematical elements.
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Optimal State Estimation
TL;DR: In this article, a list of errors in the book Optimal State Estimation, John Wiley & Sons, 2006, is presented, along with a detailed discussion of the errors.
Journal ArticleDOI
Bayesian approach to extended object and cluster tracking using random matrices
TL;DR: With simulated sensor data produced by a partly unresolvable aircraft formation the addressed phenomena are illustrated and an approximate Bayesian solution to the resulting tracking problem is proposed.