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A Gaussian mixture PHD filter for extended target tracking.

Convergence Analysis of the Gaussian Mixture Extended-target Probability Hypothesis Density Filter[J].

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Gaussian Mixture Probability Hypothesis Density Filter …

This course focuses on applications and techniques for analysis of multivariate and high dimensional data. Beginning subjects cover common multivariate techniques and dimension reduction, including principal component analysis, factor model, canonical correlation, multi-dimensional scaling, discriminant analysis, clustering, and correspondence analysis (if time permits). Further topics on statistical learning for high dimensional data and complex structures include penalized regression models (LASSO, ridge, elastic net), sparse PCA, independent component analysis, Gaussian mixture model, Expectation-Maximization methods, and random forest. Theoretical derivations will be presented with emphasis on motivations, applications, and hands-on data analysis.

The gaussian mixture probability hypothesis density filter …
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Here, it is used together with a recently proposed Gaussian mixture probability hypothesis density (GM-PHD) filter for extended target tracking, which enables estimation of not only position, orientation, and size of the extended targets, but also estimation of extended target type (i.e.

Gaussian mixture probability hypothesis density smoothing with ..

Gaussian mixture probability hypothesis ..
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4.2 Gaussian Mixture Implementation Following the derivation of a gm-phd-filter for single measurement targets in =-=[7]-=-, a phd recursion can be derived for the extended target case.

By approximating the PHD with a Gaussian mixture (GM), a practical implementation of the PHD filter is obtained, called the Gaussian mixture PHD (GM-PHD) filter =-=[13]-=-.

Fast Gaussian Mixture Probability Hypothesis Density Filter

Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking
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This course focuses on applications and techniques for analysis of multivariate and high dimensional data. Beginning subjects cover common multivariate techniques and dimension reduction, including principal component analysis, factor model, canonical correlation, multi-dimensional scaling, discriminant analysis, clustering, and correspondence analysis (if time permits). Further topics on statistical learning for high dimensional data and complex structures include penalized regression models (LASSO, ridge, elastic net), sparse PCA, independent component analysis, Gaussian mixture model, Expectation-Maximization methods, and random forest. Theoretical derivations will be presented with emphasis on motivations, applications, and hands-on data analysis.

The course will introduce the basic theory and applications for analyzing multi-dimensional data. Topics include multivariate distributions, Gaussian models, multivariate statistical inferences and applications, classifications, cluster analysis, and dimension reduction methods. Course content is subject to change in order to keep the contents up-to-date with new development in multivariate statistical techniques.

Gaussian mixture probability hypothesis density filter …
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A practical implementation of the PHD filter is provided by approximating the PHDs with Gaussian-mixtures (GM) =-=[18]-=- which results in the Gaussian-mixture PHD (GM-PHD) filter.

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filter (PHD-filter) the targets and measurements are treated as random finite sets (RFS); an implementation where the PHD-intensity is approximated using a mixture of Gaussians has been presented in =-=[3]-=-.

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