Name
Abstract | ||
|---|---|---|
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 obviating the need to use large Gaussian mixtures to approximate a uniform birth density. |
Year | Venue | Keywords |
|---|---|---|
2012 | Information Fusion | Gaussian distribution,filtering theory,probability,CPHD filtering equation,Gaussian mixture PHD filter,Gaussian mixture approximation,cardinalised probability hypothesis density filter,partially uniform target birth model,standard Gaussian mixture probability hypothesis density filter |
Field | DocType | ISBN |
Applied mathematics,Uniform distribution (continuous),Artificial intelligence,Gaussian function,Gaussian filter,Computer vision,Mathematical optimization,Gaussian random field,Generalized inverse Gaussian distribution,Gaussian,Normal-inverse Gaussian distribution,Gaussian noise,Mathematics | Conference | 978-0-9824438-4-2 |
Citations | PageRank | References |
10 | 0.71 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Beard | 1 | 145 | 7.86 |
| Ba Tuong Vo | 2 | 362 | 20.68 |
| Ba-Ngu Vo | 3 | 2408 | 175.90 |
| Sanjeev Arulampalam | 4 | 142 | 19.13 |