The standard Gaussian Mixture Probability Hypothesis Density (GMPHD) filter and Cardinalised Probability Hypothesis Density (GMCPHD) filter require the target birth model to take the form of a Gaussian mixture. Although any density (including a uniform density), can be approximated using a sum of Gaussians, this can be inefficient in practice, especially when a large number of Gaussians is required to achieve the desired accuracy. A better alternative in the case of an uninformative birth model would be to directly use a uniform density instead of a Gaussian mixture approximation. In this paper we present new forms of the GMPHD and GMCPHD filtering equations, which allow part of the target birth model to take on a uniform distribution, thus...
It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (...
The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and...
. When the projection of a collection of samples onto a subset of basis feature vectors has a Gaussi...
In this report, an alternative approach to derive the Gaussian mixture cardinalized probability hypo...
A recently established method for multi-target tracking which both estimates the time-varying number...
The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimatin...
The Probability Hypothesis Density (PHD) filter is a multiple-target filter for recursively estimati...
The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) fil...
In its classical form, the cardinalized probability hypothesis density (CPHD) filter does not model ...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for ...
This paper proposes a computationally efficient nonlinear filter that approximates the posterior pro...
The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the...
The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple t...
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter...
AbstractA new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis de...
It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (...
The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and...
. When the projection of a collection of samples onto a subset of basis feature vectors has a Gaussi...
In this report, an alternative approach to derive the Gaussian mixture cardinalized probability hypo...
A recently established method for multi-target tracking which both estimates the time-varying number...
The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimatin...
The Probability Hypothesis Density (PHD) filter is a multiple-target filter for recursively estimati...
The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) fil...
In its classical form, the cardinalized probability hypothesis density (CPHD) filter does not model ...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for ...
This paper proposes a computationally efficient nonlinear filter that approximates the posterior pro...
The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the...
The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple t...
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter...
AbstractA new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis de...
It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (...
The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and...
. When the projection of a collection of samples onto a subset of basis feature vectors has a Gaussi...