Name
Abstract | ||
|---|---|---|
The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) recursion approximates the multi-target posterior density by a multi-Bernoulli probability density, and is based on the standard measurement model that targets are points. In reality, there are many other measurement models substantially different from this. This paper represents part of our theoretical studies on a CBMeMBer filter for nonstandard multitarget measurement models. The main contribution of our work lies in the establishment of the measurement-update equations for a CBMeMBer filter based on the Poisson extended-target measurement model proposed by Gilholm et al. |
Year | Venue | Keywords |
|---|---|---|
2014 | Fusion | standard measurement model,poisson distribution,poisson extended-target measurement model,tracking filters,measurement-update equation,target tracking,cbmember filter,measurement theory,multibernoulli probability density,nonstandard target,extended targets,multitarget posterior density approximation,random finite set (rfs),multi-target tracking,cardinality balanced multitarget multibernoulli filter,probability,clutter,computational modeling,mathematical model |
Field | DocType | Citations |
Mathematical optimization,Computer science,Cardinality,Algorithm,Artificial intelligence,Poisson distribution,Probability density function,Recursion,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guanghua Zhang | 1 | 2 | 0.36 |
| Feng Lian | 2 | 14 | 3.54 |
| Chongzhao Han | 3 | 446 | 71.68 |