Name
Abstract | ||
|---|---|---|
This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter. |
Year | Venue | Keywords |
|---|---|---|
2011 | Information Fusion | Gaussian processes,Poisson distribution,target tracking,tracking filters,CPHD filter,Gaussian mixture,Poisson assumptions,cardinalized probability hypothesis density filter,extended target tracking,laser sensor,multiple measurements,CPHD,Gaussian mixture,Multiple target tracking,PHD,cardinalized,extended targets,laser,probability hypothesis density,random sets |
Field | DocType | ISBN |
Computer vision,Probability hypothesis density filter,Laser sensor,Radar tracker,Noise measurement,Computer science,Gaussian,Gaussian process,Artificial intelligence,Poisson distribution,Bayesian probability | Conference | 978-1-4577-0267-9 |
Citations | PageRank | References |
26 | 1.33 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umut Orguner | 1 | 548 | 40.11 |
| Christian Lundquist | 2 | 203 | 11.52 |
| Karl Granström | 3 | 356 | 24.53 |