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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Franken, D. Hupper, A. |
| Copyright Year | 2012 |
| Description | Author affiliation: Data Fusion Algorithms & Software, Cassidian, D-89077 Ulm, Germany (Franken, D.; Hupper, A.) |
| Abstract | Collision avoidance applications require state estimators that are able to deliver estimates of relevant quantities with sufficient quality under hard real-time constraints. In this paper, we will present a unified approach to tracking and data fusion in this context. The proposed approach is easy to implement and allows for a kinematics integration of data stemming from a variety of sensor types. Initialization, prediction, and update in a common state space extended Kalman filter are elaborated. Simulation results show strengths and weaknesses of the proposed approach. |
| Starting Page | 1246 |
| Ending Page | 1253 |
| File Size | 871479 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467304177 |
| e-ISBN | 9780982443859 |
| e-ISBN | 9780982443842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-09 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | ISIF ( Intl Society of Information Fusi |
| Subject Keyword | Equations Kalman filters Optical sensors Radar tracking Mathematical model Collision avoidance Jacobian matrices unobservable states polar tracking log-polar coordinates |
| Content Type | Text |
| Resource Type | Article |
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