Fast Gaussian Mixture Probability Hypothesis Density Filter

We presenta motion detection, dynamic, and measurement equation, as well as visualmultitarget tracking algorithm based on Gaussian mixture probabilityhypothesis density with trajectory computation in detail.

Gaussian Mixture Probability Hypothesis Density Filter …

T1 - Gaussian particle implementations of probability hypothesis density filters
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Gaussian mixture probability hypothesis density filter …

The Gaussian Mixture Probability Hypothesis Density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the prese ..."

Gaussian mixture probability hypothesis density smoothing with ..

The Gaussian Mixture Probability Hypothesis Density (GMPHD) as a closed form solution for the Probability Hypothesis Density (PHD) filter can easily provide track labels of targets in clutter.

Gaussian particle implementations of probability hypothesis density filters.
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The Gaussian Mixture Probability Hypothesis Density - …

Abstract — A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density (PHD) recursion to propagate the posterior intensity, which is a first order statistic of the random finite set of targets, in time. At present, there is no closed form solution to the PHD recursion. This work shows that under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture. More importantly, closed form recursions for propagating the means, covariances and weights of the constituent Gaussian components of the posterior intensity are derived. The proposed algorithm combines these recursions with a strategy for managing the number of Gaussian components to increase efficiency. This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters. Index Terms — Multi-target tracking, optimal filtering, point

Gaussian mixture probability hypothesis density for …

A closed-form, recursive solution to the considered problem is detailed that generalizes the Gaussian-mixture probability hypothesis density (GM-PHD) filter previously detailed in the literature.

Gaussian Mixture implementations of Probability Hypothesis Density ..