Name
Abstract | ||
|---|---|---|
The multi-model interacting algorithm, based on the Kalman filter, is defined in the linear domain. In this paper, we propose a multi-model interacting filter for the circular domain. The proposed algorithm is defined in a Bayesian framework with a von Mises circular distribution. It is used to estimate the direction of a vehicle and to define its dynamic behavior. The different models are a right turn and a left turn. The proposed circular interacting multi-model filter is applied to Map-matching. The filter processes the sensor heading measurements. For this application we assess the proposed filter for the change detection and identification of the road on which the vehicle is traveling. |
Year | Venue | Keywords |
|---|---|---|
2013 | Information Fusion | Bayes methods,filtering theory,pattern matching,road vehicles,sensor fusion,traffic engineering computing,Bayesian framework,change detection,circular domain,circular interacting multimodel filter,map matching,multimodel interacting algorithm,road identification,sensor heading measurements,vehicle direction estimation,von Mises circular distribution |
Field | DocType | ISBN |
Computer vision,Change detection,Computer science,Circular distribution,Sensor fusion,Kalman filter,Artificial intelligence,von Mises yield criterion,Pattern matching,Map matching,Machine learning,Bayesian probability | Conference | 978-605-86311-1-3 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karim El Mokhtari | 1 | 1 | 0.70 |
| Serge Reboul | 2 | 25 | 7.02 |
| Monir Azmani | 3 | 21 | 2.49 |
| Jean-Bernard Choquel | 4 | 44 | 5.67 |
| Salaheddine Hamdoune | 5 | 1 | 0.36 |
| Benaissa Amami | 6 | 4 | 2.85 |
| Mohammed Benjelloun | 7 | 163 | 24.87 |
| El Mokhtari, K. | 8 | 1 | 0.36 |