Name
Abstract | ||
|---|---|---|
This paper presents a framework for tracking extended targets which give rise to a structured set of measurements per each scan. The concept of a measurement generating point (MGP) which is defined on the boundary of each target is introduced. The tracking framework contains an hybrid state space where MGP:s and the measurements are modeled by random finite sets and target states by random vectors. The target states are assumed to be partitioned into linear and nonlinear components and a Rao-Blackwellized particle filter is used for their estimation. For each state particle, a probability hypothesis density (PHD) filter is utilized for estimating the conditional set of MGP:s given the target states. The PHD kept for each particle serves as a useful means to represent information in the set of measurements about the target states. The early results obtained show promising performance with stable target following capability and reasonable shape estimates. |
Year | Venue | Keywords |
|---|---|---|
2011 | Information Fusion | particle filtering (numerical methods),target tracking,tracking filters,PHD filter,Rao-Blackwellized particle filter,hybrid state space,measurement generating point,nonlinear components,probability hypothesis density filter,random finite sets,random vectors,shape estimation,target tracking,Kalman filter,PHD filter,Rao-Blackwellized particle filter,Tracking,data association,estimation,extended target,particle filter |
Field | DocType | ISBN |
Computer vision,Signal processing,Probability hypothesis density filter,Nonlinear system,Finite set,Computer science,Particle filter,Kalman filter,Atmospheric measurements,Artificial intelligence,State space | Conference | 978-1-4577-0267-9 |
Citations | PageRank | References |
18 | 1.05 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Lundquist | 1 | 203 | 11.52 |
| Karl Granström | 2 | 356 | 24.53 |
| Umut Orguner | 3 | 548 | 40.11 |