Name
Abstract | ||
|---|---|---|
This paper presents a novel method for tracking multiple extended objects. The shape of a single extended object is modeled with a recently developed approach called Random Hypersurface Model (RHM) that assumes a varying number of measurement sources to lie on scaled versions of the shape boundaries. This approach is extended by introducing a so-called Mixture Random Hypersurface Model (Mixture RHM), which allows for modeling multiple extended targets. Based on this model, a Gaussian-assumed Bayesian tracking method that provides the means to track and estimate shapes of multiple extended targets is derived. Simulations demonstrate the performance of the new approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CDC.2011.6161522 | 2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC) |
Keywords | Field | DocType |
Multiple Extended Object Tracking, Shape Tracking, Random Hypersurface Model | Mathematical optimization,Radar tracker,Noise measurement,Computer science,Hypersurface,Gaussian process,Bayesian probability | Conference |
ISSN | Citations | PageRank |
0743-1546 | 7 | 0.65 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcus Baum | 1 | 285 | 32.99 |
| Benjamin Noack | 2 | 168 | 23.73 |
| Uwe D. Hanebeck | 3 | 944 | 133.52 |