Name
Abstract | ||
|---|---|---|
A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by exploiting the complete in-sequence information approach and extending it to nonlinear systems. Simulation experiments on a challenging nonlinear tracking scenario show that the new approach outperforms recent state-of-the-art particle filter algorithms in some respects, despite demanding less storage requirements. |
Year | Venue | Keywords |
|---|---|---|
2012 | Information Fusion | nonlinear filters,particle filtering (numerical methods),sensor fusion,target tracking,OOSM time,in-sequence information approach,multiOOSM problem,multiple out-of-sequence measurements,multisensor target-tracking systems,nonlinear systems,nonlinear tracking scenario,particle filter based solution,state-of-the-art particle filter algorithms,storage efficient particle filters |
Field | DocType | ISBN |
Computer vision,Nonlinear system,Computer science,Control theory,Particle filter,Algorithm,Kalman filter,Sensor fusion,Atmospheric measurements,Artificial intelligence | Conference | 978-0-9824438-4-2 |
Citations | PageRank | References |
3 | 0.40 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karl Berntorp | 1 | 26 | 16.30 |
| Karl-Erik Årzén | 2 | 540 | 35.36 |
| Anders Robertsson | 3 | 431 | 60.00 |